Navigating Advanced Molecular Biology Workflows for Precision and Insight

Mastering Molecular

Biology: Tools, Techniques,

and Technologies

Optimize molecular workflows with critical

insights and advanced technologies

COMPARE

Methods and Platforms

ACHIEVE

Accurate Detection &

Quantification

EXPLORE

Emerging Innovations

MOLECULAR BIOLOGY

RESOURCE GUIDE

2 Lab Manager Molecular Biology Resource Guide

Table of Content

Navigating Advanced Workflows for Precision

and Insight ....................................................................3

Core Molecular Biology Workflows .................................5

Optimizing PCR Experiments.............................................. 6

Optimizing PCR Workflows: Four Strategies for Lab Managers.. 7

Methods for Protein Identification and Sequencing.................. 9

Next-Generation Sequencing: Library Preparation..................12

From Data to Discovery: Crafting Sequencing

Bioinformatics Workflows.................................................14

Digital Solutions to Streamline Cell Line Development Processes 18

Precision Detection: Immunoassays, Antibodies, and

Microplate Technology .................................................21

ELISA Tests: Beyond Just Antibody Detection ........................ 22

Advancing Precision in Science and Medicine with

Recombinant Antibodies ................................................. 26

Overcoming Challenges of Cell Culture

Monitoring: New Solutions to Old Problems........................ 28

Automated Microplate Technology: Application Highlights.......31

Single-Mode vs. Multimode Microplate Readers .................. 33

Omics Technologies in Molecular Biology ..................... 36

Integrating Multi-Omics Approaches in Life Science Research .. 37

New Opportunities in Proteomics.......................................41

3 Lab Manager Molecular Biology Resource Guide Introduction

Navigating Advanced

Workflows for Precision

and Insight

From clinical diagnostics and genetic research to

biotechnology development: essential strategies, tools, and

technologies for molecular biology laboratories

The ever-growing demand for precision, reproducibility, and scalability is perhaps most

acutely felt in molecular biology labs. With rapid advancements in instrumentation and the

continuous evolution of molecular techniques, lab managers and researchers must stay current

and continually refine their methods, which often means returning to the basics. Amidst all of

the exciting new developments, it’s easy to neglect the foundations.

This resource guide reviews critical molecular biology workflows, highlighting foundational

methods such as PCR, sequencing technologies, and bioinformatics tools. It offers practical

strategies to optimize workflows and enhance lab efficiency, accuracy, and data integrity, whether you’re improving existing operations, expanding into new fields, or just getting started.

4 Lab Manager Molecular Biology Resource Guide Introduction

For readers considering integrating new multi-omics approaches, this guide makes the

landscape more approachable, touching on its benefits, challenges, implementation paths,

and real-world research and clinical applications. A primer on proteomics, including mass

spectrometry, immunoassays, and emerging NGS-based approaches, is particularly helpful

for decision-makers selecting methods for new workflows.

Additional content explores new technologies for cell culture monitoring to support metabolomics, how to craft bioinformatics pipelines, and advances in detection methodologies—from

immunoassays and recombinant antibodies to microplate technologies designed for greater

throughput and reproducibility.

Framed to support decision-making from culture to analytics, this resource guide

assists labs in navigating new and established technology and techniques while

balancing budget, compliance, and expertise. It empowers lab leaders and

researchers to optimize operations, maintain robust data quality, and achieve

reproducible, impactful outcomes in their scientific endeavors.

Chapter One

Core Molecular

Biology Workflows

From PCR preparation to bioinformatics, molecular biology labs rely on optimized workflows, advanced instrumentation, and ever-evolving digital tools. Successful outcomes

depend on workflow efficiency, proper planning, and choosing the right equipment.

Beyond technical proficiency, mastering these processes requires a deep understanding

of the tools, strategies, and decisions that shape successful experiments and reproducible results.

This chapter explores core molecular biology workflows, including PCR, sequencing, and

bioinformatics. Readers will gain insight into how to select and operate different technologies, while also learning how to enhance lab efficiency, accuracy, and scalability.

5’

3’ 5’

5’

3’

Water Filter

The Polymerase Chain Reaction (PCR) method is used to amplify target DNA sequences. Primer

design, reaction mixtures, primer melting temperatures, and thermal cycling conditions contribute

to the success of the experiment. Optimizing these conditions and taking steps to prevent

contamination help to ensure successful outcomes.

The primer is a short nucleic acid sequence, required as a starting point for DNA synthesis.

Primers flank the DNA target region, with one annealing to the plus strand (5’ → 3’) and the

other annealing to the minus strand (3’ → 5’). DNA polymerase then extends the primers.

The PCR reaction mixture contains water, buffer, polymerase, dNTPs, MgCl2

, primers, and the

DNA template.

To minimize the risk of pipetting errors, prepare a PCR master mix consisting of sterile distilled

water, buffer, dNTPs, primers, and polymerase in a single tube. Aliquot the master mix, then

add MgCl2

 and DNA template.

Thermal cyclers heat and cool the reaction mixture to facilitate denaturation, annealing, and

elongation. Cycle duration and temperature depend on characteristics of the polymerases,

buffers, template size, and GC content of the DNA.

PCR EXPERIMENTS

DESIGN

MIXTURE

CONDITIONS

Primer

Reaction

Thermal Cycling

Optimizing

PRIMER DESIGN

REACTION MIXTURE

PREPARATION

~ GC content between 40-60 percent.

~ 3’ end of the primer should contain a

C or G. The hydrogen bonds in GC pairs

will promote binding and ensure DNA

ends stay annealed.

~ Avoid primer dimers: ensure the 3’ ends

of a primer set are not complimentary

~ Avoid hairpin loops: ensure the 3’ end

of a single primer is not complimentary

to other sequences in the primer

~ Avoid slipping: do not use single base

runs of more than 4, and do not use

dinucleotide repeats

~ Magnesium is a DNA polymerase cofactor.

Magnesium concentration must be

optimized to ensure maximum yield

and specificity.

~ When selecting a buffer, refer to

the guidelines provided by the

DNA polymerase supplier. Buffer

recommendations will differ depending

on the DNA polymerase in use.

~ Add reagents to the reaction mixture

in the following order:

o Sterile distilled water

o PCR buffer

o dNTPs

o MgCl2

o Forward primer

o Reverse primer

o Template DNA

o Polymerase

 Tips for

 Tips for

There are many databases and

primer design tools available to

simplify the process.

It is important to optimize

the input amount of DNA, as too

much increases the risk of nonspecific

amplification and too little wil l

reduce yields.

Primer Tm is the temperature at which half the DNA duplex dissociates and becomes single

stranded. The primer melting temperatures should differ by no more than 5°C from each

other. The conventional calculation for Tm is as follows:

2°C (A + T) + 4°C (G + C)

The nearest-neighbor thermodynamic models are superior to the conventional calculation,

as they take into consideration the stacking energy of neighboring base pairs.

TEMPERATURE Melting

There are several online resources for calculating Tm

with the nearest-neighbor models.

PCR additives, co-solvents, and modified nucleotides can lower Tm.

tm

Tm

A+ ∆S + Rln - 273.15 + 16.6LOG [NA+] Sodium ion

concentration in M

or mol L-1

Oligonucleotide

concentration in M

or mol L -1 Gas constant of

0.00199 kcal K -1.

mol-1

Entropy change in

kcal K-1. mol-1

Enthalpy change in

kcal mol-1

Conversion factor to change

the expected temperature in

Kelvins to ºC

Constant of -0.0108

kcal K-1. mol-1

Melting

temperature in ºC

∆H

C

4

=

Denature DNA

Extend Primers

Anneal Primers

SETTING THERMAL CYCLING CONDITIONS

~ The initial denaturation step occurs at 94°C to 98°C, depending on the size of the template

and DNA GC content

o High GC content (>65 percent) may require a longer incubation or higher temperature

o Buffers containing high salts may also require a higher temperature

~ The annealing step should occur approximately 5°C below the Tm of the primers

~ Extension time depends on the DNA polymerase in use. Taq DNA polymerase requires

1 min/kb and Pfu DNA polymerase requires 2 min/kb. Check manufacturer recommendations

for temperature and time parameters specific to the DNA polymerase in use.

 Tips for

PCR contamination may result in the production of unexpected amplicons. Taking precautions

throughout the experiment helps to ensure accurate results.

PCR CONTAMINATION

Avoiding

AVOIDING PCR

CONTAMINATION

~ Use PCR plates made with low binding

materials such as polypropylene to

increase recovery

~ Ensure plates, tubes, and pipette tips

are certified to be free from DNA,

DNase, RNase, and inhibitors

~ Employ a unidirectional workflow

during setup, separating pre- and postamplification space

~ Work in a space dedicated to PCR,

and work in separate PCR setup and

PCR analysis areas

~ Set aside dedicated equipment

(pipettes, centrifuge, vortex, etc.)

for PCR setup

~ Decontaminate equipment regularly

~ Open tubes carefully and as infrequently

as possible to minimize aerosols

 Tips for

7 Lab Manager Molecular Biology Resource Guide

Optimizing PCR Workflows: Four

Strategies for Lab Managers

Best practices to enhance PCR efficiency while maintaining accuracy

By Morgana Moretti, PhD

While the fundamental technique seems straightforward in

textbooks, achieving consistently high-quality PCR results

requires more than just following a protocol. It demands

efficiency, accuracy, and well-optimized workflows. This article outlines four strategies lab managers can use to improve

PCR workflows in their labs.

Strategy one: Optimize reagent

management

Expired reagents, empty stocks, and poor storage in PCR

testing mean lost time and wasted resources.

Automated inventory systems track reagent usage, expiration

dates, and storage conditions in real time. These systems can

be integrated with laboratory information management systems, providing greater oversight and avoiding unnecessary

delays or errors.

Just-in-time ordering also helps by preventing excessive stockpiling while ensuring that critical reagents are available when

needed. This strategy also minimizes waste and helps lab managers control costs. Regular audits and supplier agreements for

priority delivery are additional strategies to improve reagent

management and prevent disruptions in PCR workflows.

8 Lab Manager Molecular Biology Resource Guide

Strategy two: Reduce contamination

risks

PCR contamination commonly arises from aerosol generation that spreads amplicons. Moreover, direct contact with

reagents, whether from ungloved hands, clothing particles, or hair, can introduce contaminants to PCR reagents.

Beyond reagents, laboratory disposables and equipment can

also be sources of contaminating DNA.

To mitigate these risks, it is essential to establish dedicated preand post-PCR workspaces with separate pipettes, racks, and

consumables. Using high-quality filtered pipette tips or positive

displacement pipettes helps minimize aerosol contamination.

Regularly cleaning work surfaces with DNA-degrading

solutions and correctly disposing of contaminated plasticware

further reduces contamination risks. Additionally, enforcing

strict pipetting protocols and handling reagents with care (e.g.,

using dedicated aliquots, keeping tubes closed, and changing

gloves regularly) ensures reliable, high-quality results.

Strategy three: Leverage automation

for efficiency

Automation transforms PCR workflows by improving processes, reducing human errors, and increasing throughput.

For example, high-throughput thermal cyclers with advanced temperature control optimize amplification conditions, reducing run times while enhancing consistency.

Data interpretation, often a bottleneck in the lab, also benefits from automation. Data analysis platforms integrated into

PCR workflows can record cycle thresholds, flag abnormal

amplification curves, and generate reports that reduce data

analysis time.

Cloud-based solutions enable real-time results review,

improving collaboration and reducing troubleshooting time.

Instead of manually compiling and validating results, lab

teams can focus on refining protocols, identifying trends

in gene expression, and making data-driven decisions that

enhance efficiency and accuracy.

Strategy four: Ensure workflow

standardization and staff training

Even the best PCR protocols can fail if lab personnel don’t

follow standardized procedures consistently.

Protocols should be documented and readily accessible to

all staff. Quick-reference guides and checklists can reinforce

adherence to best practices. Additionally, regular training

sessions and competency assessments ensure that lab personnel remain proficient in PCR techniques. Finally, encouraging a culture of continuous learning through refresher

courses and peer mentoring fosters consistency and reduces

preventable errors.

Droplet digital PCR (ddPCR)

technology

Over the years, there have been many advances in

PCR technology, including quantitative PCR, real-time

PCR, and digital PCR. More recently, ddPCR has

emerged as a gold standard due to its precision,

sensitivity, and ability to determine absolute

quantification of nucleic acids. With ddPCR, the

sample is partitioned into 20,000 nanoliter-sized

droplets, enabling the measurement of thousands

of data points, compared to a single result with

traditional PCR. Additionally, the lack of requirement

for reference standards helps labs save on costs and

time while avoiding preparatory errors.

As PCR workflows continue to evolve, lab managers

may want to explore where ddPCR fits in with their

existing workflows, particularly for applications

requiring high sensitivity and quantitative accuracy.

9 Lab Manager Molecular Biology Resource Guide

Methods for Protein Identification

and Sequencing

Understand common methods, their strengths, limitations, and applications

By Morgana Moretti, PhD

There is no debate over the utility of genome sequencing

to understand the complex molecular networks of cells in

health and disease. However, understanding a protein’s

amino acid sequence is equally important. This knowledge

is essential for understanding its structure and function and

can provide valuable insights into biological processes and

disease mechanisms.

This article provides an overview of the most common

approaches to protein identification and sequencing. We

discuss how each method works, the information it offers, its

pros, cons, and best applications. Additionally, we explore

potential developments in this rapidly evolving field.

Common approaches to protein

sequencing

Mass spectrometry

Mass spectrometry is a commonly used technique for studying proteins. It involves breaking down proteins into smaller

peptides, which are separated, fragmented, ionized, and

10 Lab Manager Molecular Biology Resource Guide

captured by mass spectrometers. The end result is a mass

spectrum containing ions characteristic of the sequence of

amino acids in the selected peptide.

Mass spectrometry is versatile and can identify various

proteins and protein modifications. It can handle complex

mixtures and quantify proteins across diverse samples.

However, existing mass spectrometry methods can typically

identify fewer than 50 percent of the proteins in a complex

sample. Another major limitation of mass spectrometry is

its inability to differentiate proteins with similar masses or

structures. This low sensitivity can lead to inaccurate identifications or missing relevant proteins. Moreover, the process

requires expertise in data analysis due to the complex nature

of the results.

Immunoassays

Immunoassays, such as western blotting and enzyme-linked

immunosorbent assay (ELISA), are well-established protein

detection and quantification techniques. These methods rely

on the specificity of antibodies to recognize target proteins.

While immunoassays are not typically used to determine the

sequence of amino acids in a protein, they are valuable tools

for confirming the presence of specific proteins in a sample.

Immunoassays are sensitive and specific tests that produce

rapid results. In addition, they offer the benefit of not requiring complex equipment or the use of radioactive labels.

However, challenges with these techniques do exist. Antibody binding efficiency can be problematic, potentially

leading to false positives or negatives. Cross-reactivity with

similar proteins can also occur, impacting the accuracy of

results. Finally, western blotting and ELISA are multistep

protocols; variations and errors can occur at any step, reducing the reliability and reproducibility of these techniques.

Next-generation sequencing (NGS)

While commonly associated with DNA and RNA sequencing, NGS has extended its applications to protein sequencing, improving the speed and ease with which researchers

can correlate biological function with changes in protein

sequence and modifications.

New NGS platforms enable very sensitive experiments and

allow researchers to study proteins that are in lower abundance. This can help uncover previously hidden aspects of

cellular biology and reveal valuable insights into protein

variations and their effects.

Best applications for each method

Each method mentioned above excels in specific applications.

Immunoassays are well-suited for confirming the presence

of specific proteins, validating results from other techniques, and routine protein quantification. Scientists can

also use western blotting and ELISA techniques to identify

the presence and extent of post-translational modifications

on proteins, including phosphorylation, glycosylation, or

ubiquitination. However, detecting the broad spectrum of

post-translational modifications is limited by the number of

available antibodies, substantial specificity issues, and lot-tolot variations, leading to reproducibility problems.

In contrast, mass spectrometry identifies and quantifies

specific post-translational modifications with high sensitivity and resolution. In addition, it is ideal for large-scale

proteomic studies and characterizing proteins and protein

complexes. However, mass spectrometry often fails to identify rare proteins in a complex sample.

NGS can identify rare genetic variations, including mutations and single-nucleotide polymorphisms that can

impact protein sequences. This is particularly valuable for

understanding how these variations translate to differences

in protein function and disease susceptibility. Moreover,

NGS can excel in detecting modifications that might remain

unmatched or obscured in mass spectrometry or present

as unidentified in immunoassays. Additionally, NGS can

facilitate de novo sequencing of proteins, a method in which

the amino acid sequence of a protein is directly determined

without prior knowledge of its DNA. This is useful for discovering novel proteins or protein variations.

 “In the near-term, many studies

will likely seek to combine

the analysis of protein, DNA,

and RNA sequence data.”

11 Lab Manager Molecular Biology Resource Guide

The future: Integration of protein

sequencing with other assays

While the technologies outlined in this article offer advantages, there is a trade-off in protein analysis. Mass spectrometry can survey a large number of proteins, but its sensitivity

can vary depending on the specific approach and instrumentation employed. On the other hand, immunoassays tend

to be highly sensitive, but offer a very narrow survey of the

proteome. Methods like NGS, which enable single-molecule

sequencing of proteins, help bridge this gap by offering comprehensive proteome coverage and high sensitivity. Moreover, the ability to perform de novo sequencing of proteins

with NGS can be a game-changer in protein characterization, especially in cases where prior sequence knowledge

is limited or absent. These advantages position NGS as a

valuable addition to existing proteomic methods.

In the near term, many studies will likely seek to combine

the analysis of protein, DNA, and RNA sequence data. This

integrative approach could lead to a deeper understanding of

disease mechanisms and help identify key proteins involved

in various conditions. In addition, advancements in protein

sequencing could enable personalized treatment approaches,

tailoring interventions based on an individual’s protein profile.

As these advancements unfold, it’s essential to consider

streamlining data analysis pipelines and formulating standardized protocols. Such efforts not only enhance accessibility but also improve the reproducibility of protein sequencing

techniques. With further development, protein sequencing

methods can become sensitive, scalable, and accessible for

various proteomic applications. Additionally, they will likely

become increasingly portable, enabling their applications in

field-based settings.

12 Lab Manager Molecular Biology Resource Guide

Next-Generation Sequencing:

Library Preparation

Library preparation is a critical step in the workflow of several NGS paradigms

By Brandoch Cook, PhD and Rachel Brown, MSc

DNA sequencing is perhaps the most substantial development in molecular biology since the Watson-Crick structure

of the DNA double helix. The earliest method of nucleotide

sequencing used chemical cleavage followed by electrophoretic separation of DNA bases. Sanger sequencing improved

upon this method by employing primer extension and chain

termination, which gained primacy with its decreased reliance on toxic and radioactive agents.

Since then, pressure on the sequencing data pipeline led

quickly to considerable technological changes that far surpassed the Sanger method in terms of cost and efficiency by

flattening the workflow. The high-throughput sequencing

methods that followed, collectively known as next-generation sequencing (NGS), include several sequencing by

synthesis technologies that rapidly identify and record

nucleotide binding to complementary strands of amplified

DNA, in massively parallel synthesis reactions with a daily

throughput in the hundreds of gigabases.

Although the principle of massive parallel sequencing reactions has been shared across methods, the modes of nucleotide incorporation and fluorescence detection in the synthesis reactions differ among commercially available platforms.

13 Lab Manager Molecular Biology Resource Guide

The reagents and library preparation protocols required

for sequencing depend on the systems and models used, but

some generalities apply. Because of the sensitivity of the

technologies and the nature of much modern genomics research, success depends on high-quality, optimized libraries.

Library preparation dictates read depth (number of copies

of a given stretch of DNA sequenced), length, and coverage

(breadth of sequencing data), which need to be balanced

according to the sequencing goals. Greater read depths

improve the signal-to-noise ratio and increase confidence

in data validity. Regardless of the nature of the starting

material—genomic DNA, mRNA, DNA-protein complexes,

etc.—the precondition for generating useful NGS datasets

is a clean, robust library of nucleic acids. As in so much of

molecular biology, there is always a kit for that.

A typical, generic workflow for library preparation is as

follows: 1) sample collection, fragmentation via enzymatic

digestion or shear forces, 2) end-repair and phosphorylation

of 5’ ends, 3) ligation of oligo dT-based adapters, 4) and a

high-fidelity PCR-based amplification step to generate a

product with adapters at both ends, barcoded for identification of individual samples run as multiplex reactions. Most

library prep kits are engineered to appropriately modify and

amplify the given starting material while reducing the number of steps to accelerate sequencing workflows, maintain

sample quality, and minimize contamination. Manufacturers

typically provide a wide selection of library preparation and

sequencing kits optimized for their platform to suit a variety

of applications and sample types. Depending on the sequencing platform, third-party reagents or kits that increase

flexibility or reduce cost may be available.

Standard library prep kit protocols can usually be performed

manually or with varying degrees of automation from compact 96- or 384-channel pipette stations to high-throughput,

fully automated workstations requiring little to no manual

intervention. Automated library prep improves sequencing data by increasing consistency, accuracy, and precision

in the pipette-heavy workflows. The boost in accuracy

and precision also supports the miniaturization of sample

volumes, which can be particularly important for low-input

sequencing. Sequencing companies often work with multiple automation partners to develop validated methods for

different platforms.

While large robotic workstations are flexible in method

development and modifications, they are prohibitively expensive for many labs and best suited for very high-throughput environments. Smaller and mid-sized labs that want to

take advantage of end-to-end library prep automation that

increases walk-away time and reduces the dependency on

skilled technicians may have more luck with microfluidics

platforms. A shifting technological landscape has produced

a variety of self-contained, specialized instruments that

produce sequencing-ready libraries post-fragmentation from

low to high throughput (starting at around eight samples).

However streamlined library prep protocols become, they

require a high degree of precision and care to produce

reliable sequencing results. Labs have the benefit of many

options for methods, kits, and supporting equipment, depending on the application, chosen sequencing technology,

and degree of automation desired.

“Because of the sensitivity of the

technologies and the nature of

much modern genomics research,

success depends on highquality, optimized libraries.”

14 Lab Manager Molecular Biology Resource Guide

From Data to Discovery:

Crafting Sequencing

Bioinformatics Workflows

Discover the key factors for designing efficient, scalable, and high-quality

bioinformatics workflows for sequencing data analysis

By Jordan Willis

Modern sequencing technologies produce an intense amount

of data, but raw reads alone can’t lead to novel biological

insights. Well-designed bioinformatics workflows are now

essential for rapidly organizing sequencing data into accurate, reliable results for further analysis and interpretation.

The need for customized workflows has resulted in a variety

of methodologies, but how do labs decide upon the best

approach for their specific needs? Whether you’re optimizing an existing pipeline or designing one from scratch, this

article reviews the concepts and criteria needed to build and

maintain efficient sequencing bioinformatics workflows.

15 Lab Manager Molecular Biology Resource Guide

Understanding the advantages and

challenges of standard bioinformatics

solutions for sequencing data

Complexity, flexibility, and cost are the key factors to consider when designing a bioinformatics workflow. Overall, the

workflow should be adapted to the lab’s research objectives,

computational infrastructure, and technical expertise. Selecting the appropriate bioinformatics solution requires balancing

many factors against lab-specific needs and constraints.

Each solution has distinct strengths and challenges depending

on its workflow architecture and resource requirements. This

makes the choice context and goal dependent. The solutions fall

into three main categories that can be compared in terms of cost

considerations, data requirements and computational resources,

accuracy and reliability, and scalability and automation:

1. Do-it-yourself (DIY)

DIY bioinformatics workflows rely on in-house pipeline

development, often based on freely available open-source

tools and custom scripting. Offering the most flexibility and

transparency, DIY enables unparalleled pipeline customization and oversight. Programming languages like Python and

R are widely used for data analysis tasks, such as statistical modeling and genome analysis, while Bash serves as a

powerful shell scripting language for optimizing workflows

by automating tasks like file management and tool execution.

Bioinformatics software such as Burrows-Wheeler Aligner

(BWA), Genome Analysis Toolkit (GATK), and SAMtools

are often integrated into these workflows.

Comparison: The DIY approach is usually the most cost-effective and customizable method due to its reliance upon

open-source tools, but at the expense of investing in skilled

personnel, robust infrastructure, and ongoing maintenance.

It provides complete control over data handling, allowing for

deep customization of pipelines, benchmarking, and peer-reviewed validation. However, this level of flexibility requires

powerful computing resources and ongoing maintenance.

While DIY workflows can be highly scalable and capable of

automation, they require substantial manual configuration

and expertise to ensure long-term sustainability.

2. Third-party

Third-party bioinformatics platforms provide pre-configured analysis pipelines with user-friendly interfaces and

features like cloud-based computing. These options reduce

in-house technical expertise requirements and development

time by offering automated workflows for alignment, variant

calling, and quality control. Third-party options may include skilled customer support, built-in regulatory compliance features, and custom data security measures.

Comparison: Third-party solutions can reduce labor and

development costs but often require long-term financial

commitments. By leveraging cloud-based processing, they

reduce the need for local computational infrastructure;

however, data upload and transfer speeds can become bottlenecks, particularly with large datasets. These solutions minimize errors by providing validated and automated workflows,

Before discussing the different

solutions, several universal workflow

attributes should be highlighted:

Accuracy: The correctness of data-related

processes like variant calling, read alignment, and

quantification.

Reliability: The ability to maintain consistent

performance across datasets while minimizing errors

and batch effects.

Ease of use: The quality of user-friendly interfaces,

clear documentation, and workflow automation.

Computational efficiency: The optimization of

memory usage, processing power, data storage,

and resource allocation to minimize processing time

and costs.

Reproducibility: The ability to consistently generate

the same results when processing identical datasets

under the same computational conditions.

Integration capability: Forward and backward

compatibility with lab hardware and software.

Scalability: The capacity to efficiently upgrade

infrastructure and handle increasing data volumes.

16 Lab Manager Molecular Biology Resource Guide

but limit the control that in-house users have over pipeline

parameters. Designed for scalability, third-party platforms

often include automation features that improve efficiency in

high-throughput environments.

3. Manufacturer-provided

Many sequencing platform manufacturers offer proprietary

bioinformatics software optimized for their specific sequencing technology to ensure seamless integration and standardized data analysis. Manufacturer-provided pipelines often

include built-in quality control metrics and default parameter settings designed for comparability and reproducibility

within the lab and the broader research community.

Comparison: Manufacturer-provided solutions are often

bundled with sequencing hardware, which can reduce initial

investment costs, though additional fees may apply for extended features, software updates, or increased data storage.

These solutions are typically optimized for specific sequencing instruments, ensuring smooth data processing but often

limiting interoperability with other platforms. While they

include built-in quality control measures and incredible

efficiency within proprietary limitations, they may be less

adaptable to new sequencing methodologies or customized

analytical needs.

The importance of seamless integration

with lab software systems

Each standard solution should be designed to integrate with

laboratory informatics platforms to capitalize on validated

tools for streamlined data management, enhanced traceability, and improved reproducibility. There are three primary

types of lab software that can play a vital role in organizing,

storing, and managing sequencing data:

1. Laboratory information management system (LIMS)

LIMS platforms serve as an organizational hub for managing

metadata, overseeing tasks like reagent and workflow tracking, as well as sequencing run management. Integrating bioinformatics solutions with LIMS offers several advantages,

including automated sample tracking and traceability, which

ensures a complete history of processing steps. Workflow

automation reduces manual data entry and minimizes errors

in sequencing pipelines, improving efficiency and consistency. Additionally, LIMS enhances interoperability by facilitating communication between sequencing instruments and

downstream bioinformatics tools, resulting in a smooth and

integrated data management process.

2. Electronic lab notebook (ELN)

ELNs function as digital repositories for documentation and

data tracking. Integrating ELNs into bioinformatics pipelines

increases data consistency and reproducibility by enabling

standardized data recording, which is an important aspect of

maintaining regulatory compliance. They can also promote

collaboration in-house through shared protocols, scripts, and

documented results.

3. Scientific data management system (SDMS)

SDMSs serve as secure storage and management platforms

while ensuring compliance with data integrity standards.

Integrating an SDMS into a bioinformatics workflow helps

prevent data loss and enables controlled access to sequencing files. These systems also provide version control and

auditability, tracking changes in data processing pipelines

to provide transparency and reproducibility. Additionally,

SDMS solutions are designed to scale efficiently, handling

increasing data volumes without compromising performance, making them essential for managing large sequencing datasets.

Strategies for effective lab software

system integration

A well-integrated bioinformatics ecosystem enhances data

flow, minimizes errors, and improves the quality of sequencing data analysis. Before implementing lab software integration, labs should consider the following best practices:

1. Use standardized data formats: Ensure compatibility

between all bioinformatics tools and lab software

2. Implement API-based connectivity: Enable smooth

communication and coordination between LIMS, ELNs,

SDMS, and bioinformatics pipelines

“Complexity, flexibility, and cost

are the key factors to consider when

designing a bioinformatics workflow.”

17 Lab Manager Molecular Biology Resource Guide

3. Automate data transfers: Reduce manual intervention by

implementing scheduled data synchronization processes

4. Ensure compliance with regulations: Adhere to data integrity requirements, particularly in regulated environments

Data integrity in regulated laboratories

Ensuring data integrity is a critical requirement for sequencing bioinformatics workflows, especially in regulated environments such as clinical, pharmaceutical, and forensic laboratories. Regulatory bodies enforce strict guidelines to maintain

the reliability, traceability, and security of sequencing data.

The role of bioinformatics in data integrity compliance

Bioinformatics platforms facilitate data integrity through

built-in compliance features like audit logs, encryption, and

traceability tools. Additionally, by automating workflows, human errors are reduced, and analysis becomes standardized

to increase reproducibility. Seamless integration with LIMS,

ELN, and SDMS further enhances traceability and regulatory compliance. Maintaining data integrity is essential for

producing high-quality, reproducible sequencing results;

bioinformatics workflows must be created with this fundamental concept in mind.

Securing sequencing data integrity—best practices and

key challenges

To safeguard sequencing data integrity and ensure compliance, labs should:

1. Maintain audit trails and electronic signatures to log data

access, modifications, and approvals

2. Implement automated data validation, including quality

control checks, checksums, and redundancy measures

to detect errors

3. Use secure and redundant storage solutions with automated backups and disaster recovery protocols

Despite best efforts to maintain data integrity, several key

challenges persist in sequencing workflows that can compromise the accuracy and reliability of results:

1. Large data volumes from next-generation sequencing

require robust storage, validation, and traceability

2. Risks of data corruption or errors during transfer between

platforms, tools, and storage systems

3. Managing version control and ensuring reproducibility

amidst complex regulatory compliance requirements

Bioinformatics workflows are the

engine driving the exploration of

sequencing data

When properly designed and customized, bioinformatics

workflows are indispensable tools for analyzing sequencing

data and generating meaningful biological information.

Selecting the right bioinformatics solution requires careful

consideration of factors such as cost, data integrity, and integration with lab software, and long-term project goals should

be clearly defined to help inform decision-making. With

continuous improvements in sequencing and computational

methods, bioinformatics workflows for sequencing data will

continue to become more efficient at unlocking the world’s

biological secrets.

18 Lab Manager Molecular Biology Resource Guide

Digital Solutions to Streamline Cell

Line Development Processes

Digital tools can improve processes, data analysis, and collaboration in cell line

development

By Morgana Moretti, PhD

Cell line development (CLD) plays a crucial role in drug

discovery and the manufacturing of biopharmaceuticals—

usually recombinant proteins such as monoclonal antibodies,

bispecifics, and biosimilars.

Data silos between CLD process steps, operators, and labs

can lead to rework, bottlenecks, and issues with data integrity and compliance.

Avoiding these problems in the face of more data points than

ever requires solutions aimed at consolidating data, making

it accessible and understandable, and enabling interfaces

that maximize monitoring and collaboration. Advanced

bioinformatics tools and knowledge management systems are

becoming increasingly important in this context.

19 Lab Manager Molecular Biology Resource Guide

The digital lab approach: A game

changer in CLD

The digital lab approach involves leveraging software and

other digital tools to streamline laboratory processes, data

analysis, and communication. This approach can transform

the CLD landscape from a traditionally segmented process

to an integrated workflow.

The benefits are multifold: improved data accuracy and

transparency, efficient resource management, and enhanced

collaboration among research groups and sites.

A digital solution for every stage

of CLD

The CLD process encompasses several stages:

) Vector construction

) Vector sequence verification

) Transfection and selection of cells that have integrated

the vector genetic material

) Pool screening to identify cells with the desired traits

) Monoclonality verification

) Selection based on stability/expansion

) Antibody characterization

) Cell growth/expansion

) Creation of cell banks under Good Manufacturing

Practice standards

) Data management

In each of these stages, digital solutions offer a way to optimize the process while ensuring high-quality outputs and

strict compliance with regulatory standards.

A notable advancement in CLD is the use of high-throughput assays, which have drastically reduced the time needed

to collect essential data. These assays enable rapid quantification and characterization of biomolecules and cellular

attributes, key elements in stages like monoclonality verification and antibody characterization.

Building upon this advancement, bioinformatics software

can process, interpret, and visualize data from high-throughput assays. When combined with cloud computing platforms,

it can efficiently manage the large datasets typical in these

assays, offering scalable storage and processing power.

Laboratory information management systems (LIMS) are

also essential in this process. LIMS come into play at the

very beginning when samples are logged and tests are scheduled, and again at the end, to manage the collection, storage,

and reporting of results.

In addition, integrating reporting and analytics tools into

the CLD workflow empowers labs to generate comprehensive reports and conduct thorough analyses of assay data.

This capability is invaluable for presenting findings, sharing

insights with stakeholders, and supporting decision-making.

To further streamline the process, life cycle management

software designed for biopharmaceutical processes can help

labs manage data across the entire CLD spectrum. These

cloud-based systems provide analysis capabilities encompassing data access, reporting, visualization, and exploration.

This all-in-one approach eliminates the need for specialized

analytics tools or the expertise of data scientists.

Tips for choosing digital tools in CLD

When deciding on digital tools for CLD, prioritize those that

integrate with existing laboratory systems, protocols, and

workflows. This includes compatibility with data analysis

tools, LIMS, and electronic laboratory notebooks. Moreover,

it’s crucial to ensure that data can be transferred between

systems without loss of integrity or efficiency.

“Combining lab automation, digital

solutions, and analytics tools

appears to be the path toward faster

and more efficient outcomes in cell

line development.”

20 Lab Manager Molecular Biology Resource Guide

User-friendly interfaces, clear instructions, and responsive

customer support enhance the user experience and optimize

digital tool usage for lab staff.

Another key consideration is the security of the digital tools.

Choose software with robust security features such as strong

encryption protocols, multi-factor authentication, and secure

cloud storage options. In the context of CLD, if the data

includes personal information from human subjects, such

as genetic data from donors, the tool must comply with the

General Data Protection Regulation, Health Insurance Portability and Accountability Act, and other relevant industry

guidelines. This is necessary to meet legal requirements for

data handling and privacy.

It is also important to recognize that every laboratory has

unique needs based on the scale of its operations and the

nature of its projects. Therefore, digital tools must offer a

degree of customization to align with these specific requirements. This includes configurable workflows and data fields,

personalized reporting formats, and integration capabilities

with other tools. Software customization not only ensures it

complements the lab’s existing processes but also allows it to

adapt to the facility’s evolving needs.

The path forward

Combining lab automation, digital solutions, and analytics

tools appears to be the path toward faster and more efficient

outcomes in CLD. Lab automation increases operational

efficiency, digital solutions provide the necessary infrastructure for data management, and analytics tools offer the deep

insights needed for informed decision-making.

This integrated approach supports the development of cell lines

while addressing issues related to resources, human errors, turnaround times, redundancies, and data inconsistencies, ultimately

delivering maximum value to drug discovery organizations.

Chapter Two

Precision Detection:

Immunoassays,

Antibodies, and

Microplate Technology

Accurate detection, precise quantification, and real-time monitoring are essential capabilities in molecular biology. As scientific demands evolve, so too must the tools and

technologies supporting these workflows.

This chapter explores solutions that help labs improve data quality, throughput, and

reliability across a wide range of applications. It begins with a comprehensive overview of

ELISAs, detailing their core formats, diverse applications, and emerging innovations such

as multiplex and nanoparticle-based assays. Readers will also learn about the growing

role of recombinant antibodies in research and diagnostics, new solutions to common

cell culture monitoring challenges, and how automation and microplate technologies are

enhancing throughput, flexibility, and reproducibility across workflows.

22 Lab Manager Molecular Biology Resource Guide

ELISA Tests: Beyond Just

Antibody Detection

ELISA’s versatility, sensibility, and specificity make it an efficient method to

detect and quantify biomolecules in different settings

By Morgana Moretti, PhD

ELISA, which stands for enzyme-linked immunosorbent assay, has become a fundamental tool in basic sciences, clinical

diagnostics, and food safety testing. This article overviews

ELISA’s essential principles and various applications to give

you a comprehensive understanding of this versatile and

cost-effective method to detect and quantify biomolecules.

Principles of ELISA

ELISA uses antibodies that recognize and bind to specific proteins or molecules of interest called antigens. In an

ELISA, the antigen is immobilized on a microplate and then

complexed with an antibody linked to a reporter enzyme.

To detect the presence of a target molecule in an ELISA,

scientists measure the activity of the reporter enzyme by

incubating it with the appropriate substrate, which produces

a colorimetric or fluorescent signal that indicates the antigen’s presence. The amount of signal is proportional to the

amount of antigen present in the sample.

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Formats: Direct, indirect, sandwich, and competitive ELISA

There are four primary types of ELISA: direct, indirect, sandwich, and competitive. Overall, the choice of ELISA type will

depend on the available budget, training of lab staff, and the specific needs of the experiment, including the target molecule

and required sensitivity.

ELISA Type Detection method Sensitivity Advantages Disadvantages

Direct ELISA Directly detects the

antigen

Low to

moderate

Simple and quick, with no

secondary antibody crossreactivity

Low sensitivity compared

to other types of ELISA

Indirect ELISA Detects primary

antibody-antigen

complex

High Increased sensitivity

compared to direct

ELISA; lower antigen

concentration required

Risk of cross-reactivity

between the secondary

detection antibodies

Sandwich ELISA Antigens are

sandwiched between

two layers of antibodies

(capture and detection

antibodies)

High Detects low

concentrations of

antigens; the highest

sensitivity among all the

ELISA types

Time-consuming and

expensive

Competitive ELISA Competition between

labeled and unlabeled

antigens

High Less sample purification

is needed; can measure

a large range of antigens

in a sample; can detect

small antigens

Low specificity and

cannot be used in dilute

samples

ELISA Flowchart

Direct ELISA

Directly detects

the antigen

Indirect ELISA

Detects primary

 antibody-antigen complex

Competitive ELISA Competition between antigen

& reference antigen for the

labeled antibody

Sandwich ELISA

Antigen is sandwiched

between antibodies

(capture & detection)

Prepare samples

Wash and block microplate

Wash microplate

Wash microplate

Add sample

Add primary antibody Add sample

Add conjugated secondary antibody

Coat wells with reference antigen

Add antigen-antibody mixture

Coat wells with primary antibody

Add substrate

Read microplate

Calculate results

1° Ab

Ag

1° Ab

2° Ab

Ag 1° Ab

2° Ab

Ag 1° Ab

2° Ab

Reference Ag

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ELISA Unlocked: A Visual

Guide for Effective

Experimentation

A clear understanding of how each method works,

along with its pros and cons, is essential for navigating

these complexities. Our ELISA Flowchart offers a

detailed step-by-step guide for each method, with

insights into which format to use and when.

24 Lab Manager Molecular Biology Resource Guide

ELISA applications: From basic sciences

to environmental analysis

ELISA has many uses. Some applications of this technique include:

Diagnostic testing and investigation of disease

biomarkers

In the diagnostic space, ELISA can detect and quantify

analytes in biological samples like serum, plasma, and urine,

with applications ranging from viral and bacterial detection

to pregnancy testing.

ELISA can also be used to investigate disease biomarkers.

For example, in an article published in Nature Aging in

February 2023, researchers used sandwich ELISA to measure circulating growth differentiation factor 11 (GDF11)

levels in the blood of patients with depression.1

 The authors

found that people with depression have a decrease in GDF11

compared to healthy controls and raised the possibility that

serum GDF11 levels can be a potential biomarker for depression in humans.

Additionally, ELISA is a method used to measure the

concentration of disease biomarkers over time, which can

provide insights into disease progression and the effectiveness of treatment.

For example, ELISA-based assays use specific antibodies

that recognize and bind to HIV proteins, allowing for the

measurement of HIV concentration in the patient’s blood. By

using ELISA to measure viral load over time in HIV-positive

patients, healthcare providers can monitor disease progression and treatment effectiveness.

ELISA can also detect and estimate the levels of tumor

markers, such as the prostate-specific antigen, which has

revolutionized the diagnosis, treatment, and monitoring of

patients with prostate cancer. In addition, ELISA can identify autoantibodies indicative of conditions such as lupus and

rheumatoid arthritis.

Food safety

ELISA is intensively applied in food safety as it can detect

bacteria, parasites, pesticides, and other food contaminants.2

For example, ELISA is a popular method to detect allergenic

substances, including egg whites, peanuts, and milk. Commercial kits can detect allergen proteins in various sample

types, including clean-in-place final rinse water, food ingredients, and processed food products.

By detecting allergens and other food contaminants, manufacturers can take appropriate action to prevent cross-contamination and ensure that their products remain safe.

Environmental monitoring

ELISA has proven to be a valuable tool to quantify surfactants, endocrine disruptors, estrogens, and persistent organic

pollutants such as dioxins and polychlorinated biphenyls in

environmental and industrial wastes.3

 This can help identify

potential pollution sources and develop strategies to protect

public health and the environment.

Compared to instrumental analytical methods such as

high-performance liquid chromatography and liquid chromatography-mass spectrometry, ELISA offers considerable

advantages because of ease of handling, fast measurement,

high sample turnover, and acceptable costs.

Emerging ELISA techniques: Potentials

and pitfalls

ELISA has advanced significantly in recent years to meet

consumers’ demands for higher productivity, greater

25 Lab Manager Molecular Biology Resource Guide

sensitivity, and faster results. One such advancement is the

development of multiplex ELISA, which allows multiple

analytes to be simultaneously detected and quantified in a

single sample.4

In a multiplex ELISA, multiple capture antibodies, each

specific to a different target analyte, are immobilized onto a

solid support such as a microplate. After the sample is added,

detection antibodies labeled with fluorescent or luminescent

tags are introduced to the mixture. If a specific analyte is

present, the detection antibody will bind to it, allowing for

its identification.

The availability of commercial multiplex immunoassays

for research applications is expanding rapidly and offers

the benefit of reduced time and smaller volumes needed to

conduct an analysis. However, the transition to clinical use

requires rigorous validation and standardization to ensure

reliable and accurate results.

Nanoparticle-based ELISA is another recent modification of

traditional ELISA.5

 In this approach, nanoparticles are coated with antibodies or antigens to capture target molecules

from a sample. The captured molecules are then detected

using a secondary antibody conjugated to an enzyme or

fluorescent molecule.

Nanoparticle-based ELISA offers several advantages over

traditional ELISA, including increased sensitivity and specificity due to the enhanced binding capacity of nanoparticles,

as well as the ability to detect multiple analytes simultaneously. This technique has shown promising results but is

still in the early stages of development and requires further

optimization and validation before it becomes widely used in

clinical and environmental analysis.

Recent significant innovations, coupled with the extensive

global expertise in ELISA, promise to expand this technique’s capabilities, sensitivity, and automation processing,

making it an even more attractive choice for analyte detection in labs worldwide.

References:

1. “Systemic GDF11 attenuates depression-like phenotype in

aged mice via stimulation of neuronal autophagy.” https://

www.nature.com/articles/s43587-022-00352-3

2. “Application of nano-ELISA in food analysis: Recent

advances and challenges.” https://www.sciencedirect.com/

science/article/abs/pii/S0165993618305399

3. “The use of enzyme-linked immunosorbent assays

(ELISA) for the determination of pollutants in environmental and industrial wastes.” https://pubmed.ncbi.nlm.

nih.gov/17302299/

4. “ELISA in the multiplex era: Potentials and pitfalls.”

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6680274/

5. “Recent improvements in enzyme-linked immunosorbent

assays based on nanomaterials.” https://pubmed.ncbi.nlm.

nih.gov/33303168/

“Overall, the choice of ELISA type

will depend on the available budget,

training of lab staff, and the specific

needs of the experiment, including

the target molecule and required

sensitivity.”

26 Lab Manager Molecular Biology Resource Guide

Advancing Precision in

Science and Medicine with

Recombinant Antibodies

From disease therapy to the detection of foodborne pathogens, discover

the innovative world of recombinant antibodies

By Morgana Moretti, PhD

Recombinant antibodies, which are also called genetically

engineered antibodies, have emerged as an innovative technology with applications in research, diagnosis, and therapy.

This article provides a general background to recombinant

antibodies and an overview of their applications, potential

benefits, and limitations.

Recombinant vs. traditional antibodies:

What is the difference?

The key differences between traditional and recombinant

antibodies are the production method and the antibodies’

resulting properties.

In traditional antibody production, researchers immunize animals with the target antigen to stimulate antibody

production. Antibodies are then harvested from the animal’s

blood or serum and purified.

Recombinant antibodies are produced in vitro rather than by

infecting living organisms. The technology typically involves

obtaining antibody genes from source cells, including hybridomas and phage display libraries, amplifying and cloning

the genes into an expression vector, introducing the vector

into a host (bacteria, yeast, or mammalian cell lines), and

achieving adequate expression of the functional antibody.1,2

27 Lab Manager Molecular Biology Resource Guide

Optionally, manufacturers can engineer the recombinant

antibodies to have specific properties, such as higher binding

affinity, improved stability, or the ability to target particular

cell types (which is not possible with traditional antibodies).

Applications of recombinant antibodies

Recombinant antibodies are a fast-growing class of biopharmaceutical products with many therapeutic applications.

For example, the anti-HER2 antibody trastuzumab has been

used to treat HER2-positive breast cancer. Another example

is the anti-EGFR antibody cetuximab, which is effective

against colorectal and head and neck cancer.3,4

In the biotechnology industry, scientists use recombinant antibodies to study protein structures and signal transduction

pathways. For example, Oregon Health & Science University

researchers have used recombinant antibodies to study the

structure and function of the human serotonin transporter.5

Recombinant antibodies can also be applied to detect foodborne pathogens, toxins, antibiotics, pesticides, and mycotoxins in food.6

Precise design and efficient production

The ability to precisely design and engineer recombinant

antibodies with desired properties, such as increased affinity,

specificity, and stability, makes them more versatile and

valuable in different applications.

Manufacturers can produce recombinant antibodies in large

quantities and with consistent quality, making them suitable

for commercial production. In addition, laboratories can produce an antigen-specific recombinant antibody in as few as

eight weeks. This compares favorably to historical timelines

of four months to produce an antibody using immunization

processes. The production of recombinant antibodies is highly reproducible since it relies on a known and defined DNA

sequence. Moreover, recombinant antibodies are genetically

stable. Unlike traditional antibody production, they do not

exhibit genetic drift, antibody expression variation, and antibody sequence mutation that can cause non-specific binding.

The high cost of recombinant

antibodies: A barrier to accessibility

Recombinant antibodies have advantages over traditional

antibodies that can improve their quality in specific applications. But making high-quality antibodies requires skilled

labor and increased manufacturing costs.

The high cost can make recombinant antibody-based therapies inaccessible to many patients who need them. For

instance, in Canada, trastuzumab costs $49,915 and $28,350

per patient treated in the adjuvant and metastatic breast cancer

settings, respectively.7

 This corresponds to an average increase

in healthcare expenditure of approximately 19 percent over

conventional management without recombinant antibodies.

Recombinant antibodies represent a powerful technology

with increasing applications. As the field evolves, researchers

and industry professionals will explore new ways to optimize

and use this technology. We can expect new and transformative discoveries that will shape the fields of science

and medicine.

References:

1. “Hybridoma technology; advancements, clinical significance, and future aspects.” https://www.ncbi.nlm.nih.gov/

pmc/articles/PMC8521504/

2. “Recombinant antibodies for diagnostics and therapy

against pathogens and toxins generated by phage display.”

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168043/

3. “Trastuzumab for early-stage, HER2-postive breast cancer: a meta-analysis of 13 864 women in seven randomised

trials.” https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(21)00288-6/fulltext

4. “Radiotherapy plus Cetuximab for Squamous-Cell Carcinoma of the Head and Neck.” https://www.nejm.org/doi/

full/10.1056/nejmoa053422

5. “X-ray structures and mechanism of the human serotonin

receptor.” https://www.nature.com/articles/nature17629

6. “Recombinant antibodies and their use for food immunoanalysis.” https://link.springer.com/article/10.1007/

s00216-021-03619-7

7. “The cost burden of trastuzumab and bevacizumab therapy for solid tumours in Canada.” https://www.ncbi.nlm.

nih.gov/pmc/articles/PMC2442764/

28 Lab Manager Molecular Biology Resource Guide

Overcoming Challenges of Cell

Culture Monitoring: New Solutions

to Old Problems

Advanced sensor technologies can help researchers overcome difficulties

By Jordan Willis

Researchers conducting metabolic studies have always faced

a common challenge: the development and implementation

of accurate instrumentation and rigorous methodologies for

the continuous monitoring of cell cultures.

The solutions for this challenge must ensure that the data

gathered from cell monitoring is predictive of in vitro biological model systems and performed in a way that doesn’t

compromise cell cultures or data integrity. This is crucial

for research and development labs that depend on having

high-quality and reproducible data.

This article will identify the difficulties inherent to traditional methods of cell culture analysis and discuss solutions

to the challenges facing cell-culture-based laboratorians.

Existing methods of cell culture analysis

Cell cultures are a component of many biological research efforts, but are particularly crucial to the study of metabolism.

Metabolic studies typically require specialized equipment,

such as incubators, meant to imitate physiological conditions

that can vary depending on the cell types or project goals.

29 Lab Manager Molecular Biology Resource Guide

Cell culturing is usually performed on either adherent cells,

typically used for examination of cell-to-cell interactions, or

suspension cells, which grow freely in liquid medium. Regardless of how researchers grow the cells, modern standards

dictate that the cultures need continuous monitoring, which

can easily lead to complications when monitoring equipment

fails or due to the invasiveness of the monitoring technique.

Traditional methods of cell culture analysis each have their

difficulties in application. To address these issues, multiple

methods can be applied in conjunction to compensate for the

shortfalls affecting singular methods, for example, combining

the use of visual inspection via microscopy with offline sampling. Microscopy enables assessment of cell morphology,

culture density, and general health using an instrument that

is immediate and non-invasive. But this approach is limited

in the quantitative data collected, giving no information

about cell viability or metabolic data.

To offset this problem, offline sampling can gather this missing data (e.g., monitor oxygen levels, nutrient abundance,

etc.), but it risks disrupting the culture, introducing non-experimental stressors, and increasing the probability

of contamination.

Further, when combining methodologies, considerations must

include the added complexity and cost of using multiple analytical techniques, whether simultaneously or intermittently,

to gather the necessary data. For example, it’s not always clear

how to minimize experimental bias by timing the application

of non-invasive and invasive monitoring techniques.

Issues in cell culture monitoring are not only logistical, but

also practical. These challenges tend to be about problems

with the sensors used in many methods and how their performance can be affected over the duration of the study.

For example, data-gathering sensors can be prone to fouling

over the course of a measurement period due to the culture

medium accumulating on them, especially in the case of

dissolved oxygen probes. Another challenge is the inevitable

degradation of the sensor itself, usually in the form of wear

and tear from physical or chemical stressors. Furthermore, it

may be difficult to adequately match the calibration environment to the environment of the culture medium. Should

the two environments not match in a significant way (e.g.,

a temperature discrepancy), then the data gained from that

sensor can be skewed and jeopardized.

New technologies prevent degradation,

provide multiplexing

The challenges above display the need for using robust and

powerful technologies coupled with careful planning to

ensure cell culture integrity while collecting valuable data

from accurate, well-maintained sensors and equipment.

Manufacturers have begun introducing new technologies

to address these many challenges. One such technology is

advanced sensors that offer consistent measurement data

via non-invasive optical sensors that don’t require physical

contact with the culture medium.

Other concerns include:

• The recurring cost of purchasing reagents

• High initial cost of equipment

• The hiring of skilled personnel to calibrate and

maintain this equipment

• How measurement tools might be affected by

day-to-day operations or by the cultures they

measure

30 Lab Manager Molecular Biology Resource Guide

These sensors are constructed of materials that make them

resistant to sensor fouling, prevent degradation, and mitigate errors like temperature drift. Additionally, sensors that

are capable of multiplexing and simultaneously recording

multiple analytes or environmental conditions can reduce

the device count within culture chambers, saving space and

reducing the total surface area for potential contamination.

Furthermore, sturdier sensors that don’t require replacement

or recalibration enhance closed systems, making them far

less prone to contamination risks and additional stressors.

New sensors step in to aid cell culture

monitoring

Advanced sensor technologies present exciting solutions for

improving the quality of research data in ways that circumvent many traditional challenges of cell culture monitoring.

The limitations of individual methods, the risk of contamination during examination or calibration, the costs associated with each method, and current-generation sensor issues

are all important factors to consider when designing successful cell culture monitoring and metabolic studies.

The advent of newer technologies that can mitigate or

eliminate these issues are incredible advancements, which

can be used to gather increasingly complex, sensitive, and

high-quality data that would facilitate further discovery in

the biological and pharmaceutical research fields.

“Sturdier sensors that don’t require

replacement or recalibration enhance

closed systems, making them far

less prone to contamination risks

and additional stressors.”

31 Lab Manager Molecular Biology Resource Guide

Automated Microplate

Technology: Application Highlights

Microplate technology and automation for immunoassays, protein research, and

next-generation sequencing

By Mike May, PhD and Michelle Dotzert, PhD

Microplate readers are integral for workflows in a variety of

fields of research and clinical diagnostics. Their versatility

is particularly useful for molecular biology research, where

ongoing improvements in technology and automation support immunoassays, protein research, and next-generation

sequencing (NGS) applications, among others.

Automating immunoassays

The enzyme-linked immunosorbent assay (ELISA) detects

and quantifies a target protein in a sample. Applications

range from biomedical research to clinical diagnostics,

food safety, and more. The assay involves incubation and

washing steps, making it complicated to run multiple assays

simultaneously. Incorporating a microplate handler into the

workflow can improve throughput and consistency. Combining automation with scheduling software can ensure proper

timing when processing multiple plates in a batch.

32 Lab Manager Molecular Biology Resource Guide

For certain assays, it is imperative to maintain a controlled

environment for the plates while they are being moved.

Some automated incubators can move plates to liquid dispensers and return them to the incubator for further imaging

or detection processes. Other sophisticated solutions use

machine vision—a combination of cameras, sensors, and

algorithms that enable the instrument to perceive its surroundings and make decisions based on this data—to move

plates within a workflow.

Ultimately, automating an immunoassay requires thinking

about how to fit in the robotics. Factors such as footprint, device integration capability, and software must be considered.

In addition to meeting the lab’s needs for current assays, it is

important to consider potential future assays and how this

may impact automation needs. With the range of options,

most scientists can find something that fits many—if not

all—of their needs in automating immunoassays.

Solutions for protein research

Faced with a plate of multiple wells, a scientist in search of

proteins often turns to automated methods. The likely tasks

range from confirming the presence of a specific protein to

quantifying or isolating it. Using a process that includes a

handler makes the results easier to collect as well as more

accurate and repeatable.

In a pharmaceutical company’s bioanalytical labs, scientists

typically know which proteins are of interest. Then they

build targeted methods for them and use microplate handlers

to do the sample preparation. For example, when performing

targeted quantitation of endogenous or exogenous proteins,

a liquid handler can perform most of the processing—aliquoting unknown samples, preparing standards and quality

control samples, and adding reagents for affinity capture and

protein digestion.

Accuracy and precision in the liquid-handling steps, a robust

system, and ease of use for the operator are key attributes of

automation technology. Microplate handling also requires

flexibility and integration. The key challenge for protein applications revolves around integrating various platforms and

timing the exchanges of samples and processes. Scheduling

software can be used for systems that integrate microplate

handlers with other devices such as incubators.

Getting the most from analyzing proteins in microplates

requires teamwork. Many platforms must work together,

and that requires hardware and software that combine in a

system and work as one.

Plate readers support NGS applications

Microplate readers are instrumental in NGS workflows,

particularly in the preparation and quality control stages.

They are used for quantifying DNA libraries, assessing their

quality, and ensuring consistency in sequencing outputs.

In selecting a microplate reader, various features should be

considered, including recent improvements. Some of the

most exciting advances in microplate reader technology for

NGS applications improve the user experience and workflow.

For example, automated readers often include features

for precise temperature control and mixing. Maintaining

optimal conditions helps to ensure reliable and reproducible results. Automated readers designed to handle multiple plates simultaneously can provide higher throughput

capabilities, accelerating sample processing and increasing

efficiency. Finally, integrating readers with robotic systems

and liquid-handling platforms aids in workflow automation.

The continued improvement of microplate readers and

supporting automation technology offers many benefits for

molecular biology applications. Considering current and

future needs will help to ensure the lab’s productivity well

into the future.

“Accuracy and precision in the

liquid-handling steps, a robust

system, and ease of use for the

operator are key attributes of

automation technology.”

33 Lab Manager Molecular Biology Resource Guide

Single-Mode vs. Multimode

Microplate Readers

The choice to maximize efficiency and capability on a multimode reader is

dependent on present and future needs

By Brandoch Cook, PhD

Microplate-based applications tend to fit into two experimental

streams. The first involves discrete, end-point measurements of

changes in sample parameters (color, brightness, or fluorescence)

as surrogates for intrinsic properties of biological materials that

activate, quench, or metabolize substrates. These measurements

are pillars of laboratory bioscience. One can use them to quickly

obtain protein, RNA, and DNA concentrations in finite series of

samples and compare them to standard curves.

The second stream fits into a drive to automate those workflows and to incorporate dynamic labels and technologies

to run limitless numbers of samples through cutting-edge

screening and characterization assays. In this second stream,

there is a premium on throughput, miniaturization, reproducibility, and the flexibility to add or develop new assays

based on emerging technology.

34 Lab Manager Molecular Biology Resource Guide

The types of microplate readers available fall along two lines

appropriate to those experimental streams. A single-mode

reader only handles one of the following: absorbance, fluorescence, or luminescence. In contrast, a multimode reader

combines at least two, if not all three, platforms in one system. Moreover, additional capabilities can handle dynamic,

real-time assays based on variations of them. Multimode

instruments usually come with bigger price tags than their

single-mode counterparts. However, users should primarily

base purchasing decisions on the current and predicted diversity of their workflows and how different applications will

drive needs for different read modes.

Single-mode readers: Absorbance,

fluorescence, luminescence

A single-mode absorbance reader usually uses an internal

monochromator to quickly split focused light across a wide

spectrum (typically 230–1,000 nm) and select a wavelength

particular to the target being measured. This target is often

represented by a colorimetric change forced by the binding of

a dye or chromogenic reagent. A classic example is measuring

protein concentrations via assays such as Bradford or bicinchoninic acid. Other common absorbance assays include ones

for the determination of nucleic acid concentrations and the

quantification of ELISA-based antibody-ligand interactions.

A fluorescence reader can also use monochromator technology to resolve fluorescent signal intensity, although with a

dual system corresponding to excitation and emission wavelengths. There is a higher degree of sensitivity compared

to absorbance, allowing one to measure comparatively rare

events in a sample rather than an overall change in the whole

sample. Therefore, fluorescence readers are particularly

suitable for cell-based assays that use reporters to quantify expression of engineered fusion proteins. Fluorescence

intensity assays can also be used to examine cell populations

for death, survival, and proliferation using dyes and antibodies that can tag fragmented DNA, regulatory proteins,

or newly incorporated nucleotides. They can also measure

changes in protein signaling based on quantification of

fluorescent dyes that bind downstream effectors such as calcium. The availability of newer fluorophores with narrower

absorbance and emission ranges can extend the capabilities

of fluorescent readers into multiplex analysis of more complicated expression patterns.

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A luminescence reader can quantify the glow or flash of a

naturally emitting sample or of an engineered reporter with

much greater sensitivity than even a fluorescent measurement. It usually achieves this through the use of filters, rather

than a monochromator, favoring sensitivity of detection

over flexibility in choice of wavelength. For flash-based

assays with short half-lives, instruments use xenon lamps

with photomultiplier tubes, but they must be modified to

include injectors to properly control assay timing. The most

famous luminescence assay uses a fragment of luciferase, the

protein that makes fireflies glow, as a reporter to measure the

activation of gene promoters, or the formation and dissolution of protein complexes. This technology can also be

extended to measure how close drug molecules are to their

protein targets.

Multimode readers

Although single-mode microplate readers satisfy many

standard laboratory workflows, and even some specialized

ones, it is multimode readers that really extend and expand

capabilities into new areas of discovery, particularly via

high-throughput screening (HTS) applications. At minimum, a multimode reader provides the ability to choose

among absorbance, fluorescence, and luminescence with

one machine. Because these three platforms use different

light-splitting technologies, the user can choose between

monochromator and filter modes, with some models employing large and extensive filter wheels, or dual photomultipliers

to improve the available wavelength range. This allows a

user to optimize assays for sensitivity, speed, and accuracy.

Additionally, it imparts the capability to multiplex several

different signals to analyze the response or status of multiple

proteins, reporters, or interactions.

Where multimode readers distinguish themselves from single-mode readers is their extension of fluorescence and luminescence capabilities, particularly to plug into HTS-oriented

workflows. These capabilities include fluorescence polarization, time-resolved fluorescence, fluorescent resonance

energy transfer, and ALPHAScreen (amplified luminescent

proximity homogeneous assay).

“Although single-mode microplate

readers satisfy many standard

laboratory workflows, and even some

specialized ones, it is multimode

readers that really extend and

expand capabilities into new areas

of discovery, particularly via highthroughput screening applications.”

Fluorescence polarization: A fluorescent reporter

hits a target molecule, altering its rotation and the

trajectory of plane-polarized light.

Time-resolved fluorescence: Specialized lanthanide

chelate fluorophores with wider Stokes shifts cause

emission to follow excitation after a delay, rather

than occurring almost simultaneously. This improves

sensitivity and signal-to-noise ratios, which results in

better z prime numbers while validating screening

strategies, in comparison to end-point fluorescence

intensity readings.

Fluorescent resonance energy transfer (FRET):

Quantification of light energy transfer between

donor and acceptor fluorophores functions as a

surrogate for the distance between substances

conjugated to them.

ALPHAScreen: Operates on analogous principles but

uses laser excitation on donor beads to kick ambient

oxygen into a higher energy state that decays across

space but causes high-intensity acceptor bead

emission if the two beads are close enough. Among

the wide array of applications for these assay

platforms, streamlining identification and validation of

therapeutic molecules and antibodies is paramount.

Chapter Three

Omics Technologies

in Molecular Biology

Multi-omics is a powerful approach to understanding the complexity of biological systems, enabling a more comprehensive view than any single approach. At the same time,

innovations in protein sequencing are expanding the possibilities for proteomics, a field

that has historically lagged behind genomics in terms of accessibility and adoption.

This chapter explores genomics, transcriptomics, proteomics, and metabolomics, providing insights into the contributions of each. It highlights the benefits and challenges of

integrating multi-omics data, examines real-world applications (e.g., biomarker discovery,

personalized medicine, and environmental monitoring), and illustrates the power of these

approaches in advancing research and translational science. Readers will then learn about

the accessibility challenges in proteomics and how innovations like single-molecule protein sequencing are opening new doors.

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Integrating Multi-Omics Approaches

in Life Science Research

Learn how omics technologies are accelerating research breakthroughs

By Marnie Willman

The life sciences have undergone a technological revolution

driven by the development of various omics approaches

such as genomics, proteomics, and metabolomics. These

tools have transformed research by offering unprecedented insights into the molecular underpinnings of health

and disease.

Each omics technology reveals a piece of the puzzle, but the

real power lies in integrating these different datasets—a

concept known as multi-omics. By combining insights from

multiple molecular layers, researchers can form a comprehensive view of complex biological systems and gain deeper

insights into disease mechanisms.

Overview of omics technologies

Genomics

The development of next-generation sequencing (NGS)

technologies has propelled genomics forward, allowing

researchers to sequence entire genomes quickly and cost-effectively. NGS platforms provide high-resolution data that

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enable the identification of genetic variations, including

mutations associated with diseases like cancer. Recently,

genomics has expanded into areas such as epigenomics and

structural genomics, enabling scientists to study not only the

genetic code but also the regulatory mechanisms controlling

gene expression and large-scale genomic architecture.

Proteomics

Since proteins carry out most cellular functions, studying

their expression patterns can reveal much about disease

processes and cellular health. Mass spectrometry and

protein arrays are core techniques in proteomics, allowing

the quantification and identification of thousands of proteins

from complex biological samples. Recent advancements in

proteomics include quantitative proteomics and post-translational modification analysis, providing critical insights into

how proteins are regulated and how their activity can change

in disease states. Proteomics is particularly valuable in drug

development and biomarker discovery.

Metabolomics

By studying the metabolome, we can uncover changes in

metabolic pathways associated with disease, nutrition, or environmental exposure. Techniques such as nuclear magnetic

resonance spectroscopy and liquid chromatography-mass

spectrometry are commonly used to detect and quantify

metabolites. Advances in targeted and untargeted approaches

allow researchers to either focus on specific metabolites or

perform a broad sweep of the metabolic landscape. Metabolomics is key to understanding diseases like diabetes, cardiovascular disorders, and metabolic syndromes.

Other omics

Other omics fields, such as transcriptomics and epigenomics,

provide additional layers of information. Techniques like

RNA sequencing allow researchers to measure transcript

levels and analyze differential expression patterns. Epigenomics investigates heritable changes in gene function,

focusing on modifications such as DNA methylation and

histone modification that can alter gene expression without

changing the underlying genetic code. These omics approaches add further depth to our understanding of cellular

processes and disease mechanisms.

The benefits of multi-omics integration

Comprehensive view of biological systems

By integrating data from multiple omics layers, researchers

can gain a holistic view of cellular functions and molecular

interactions. For instance, genomics can reveal mutations

present in a cell, but combining it with proteomics can show

how those mutations alter protein expression and activity.

Metabolomics provides additional context by showing how

these changes impact metabolic pathways. This integrated

approach offers a detailed understanding of biological systems

and disease mechanisms that would be missed using a single

omics approach. Cancer research has particularly benefited

where integrating genomics with proteomics has led to new

insights into the molecular pathways driving tumor growth.

Enhanced disease mechanism understanding

Multi-omics integration has proven powerful in uncovering

the underlying mechanisms of complex diseases. In oncology, multi-omics approaches have revealed how genetic mutations, protein expression changes, and metabolic shifts work

together to drive disease progression. This detailed understanding enables researchers to map signaling networks that

control cell growth and survival, identifying potential therapeutic targets that might be overlooked when using a single

omics approach. Multi-omics research is also advancing our

understanding of neurodegenerative diseases, autoimmune

disorders, and cardiovascular diseases, where complex molecular changes occur across different biological layers.

Improved biomarker discovery and personalized

medicine

Combining genomic and proteomic data has led to the identification of new biomarkers for cancer and cardiovascular

diseases. These biomarkers enable the development of more

precise diagnostic tools and real-time patient monitoring.

Multi-omics is also paving the way for personalized medicine, where treatment plans are tailored to individual molec-

 “By integrating data from multiple

omics layers, researchers can gain a

holistic view of cellular functions and

molecular interactions.”

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ular profiles. Returning to the previous oncology example,

multi-omics data allows researchers to stratify patients into

subgroups based on their unique molecular characteristics,

leading to more targeted therapies and better outcomes.

Challenges and solutions in multi-omics

integration

Data complexity and management

One of the greatest challenges in multi-omics research is the

vast volume and complexity of data generated by each omics

technology. Genomic datasets can contain millions of data

points, and when combined with proteomic and metabolomic data, the complexity increases. Managing, storing, and

analyzing such vast data requires robust bioinformatics tools.

New computational pipelines and data integration frameworks are helping address these challenges by processing and

standardizing data from multiple omics sources, enabling

researchers to draw meaningful conclusions.

Interpreting multi-omics data

Interpreting multi-omics data is also challenging because

researchers must correlate findings from different molecular

layers. Changes in gene expression may not correspond directly to changes in protein levels due to post-transcriptional

regulation. Advanced integration algorithms and statistical

models are being developed to identify relationships between

omics datasets, bridging gaps between genomics, proteomics,

and metabolomics, and creating unified biological models

that reflect the interaction between genes, proteins, and

metabolites.

Cost and resource considerations

Conducting multi-omics studies can be resource-intensive,

requiring multiple high-throughput platforms and advanced

data analysis tools. However, technological advancements,

such as miniaturized sequencing platforms and automation

technologies, are making these techniques more cost-effective. Cloud-based bioinformatics solutions also provide

scalable data processing options, reducing the need for

specialized infrastructure and increasing accessibility for a

broader range of researchers.

Future directions and emerging trends

Advances in technology

Several technological innovations are shaping the future of

multi-omics, including the development of single-cell omics.

Traditional bulk analyses often average out molecular signals

across populations of cells, but single-cell technologies, such

as single-cell RNA sequencing, allow for the exploration

of cellular heterogeneity. As single-cell techniques become more scalable, they will continue to play a key role in

multi-omics studies.

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Real-time in vivo monitoring is another emerging trend.

Technologies that can analyze omics data in real time within

living organisms allow for dynamic tracking of disease progression and treatment responses. Innovations like wearable

biosensors and microfluidic chips enable continuous monitoring of molecular changes, bringing multi-omics into

real-time healthcare and disease monitoring.

Long-read sequencing technologies are improving data

quality in multi-omics studies by accurately sequencing

complex regions of the genome. These technologies enhance

our understanding of gene regulation processes and provide

deeper insights into structural genomic variations.

Integration with artificial intelligence

Artificial intelligence (AI) and machine learning are playing

an increasingly important role in analyzing and interpreting multi-omics data. AI models can detect patterns across

genomics, proteomics, metabolomics, and other datasets that

traditional methods might miss. AI-driven predictive models

are also being developed to forecast patient responses to

treatments based on multi-omics profiles, which is advancing the field of precision medicine. AI is further integrated

into data analysis platforms, automating the process of data

integration and interpretation, making multi-omics more

accessible and efficient.

Potential applications and implications

The integration of multi-omics is poised to considerably

advance disease research, drug discovery, and personalized

medicine. In neurodegenerative diseases like Alzheimer’s

and Parkinson’s, multi-omics is uncovering the complex

interplay between genetic, protein, and metabolic changes

that contribute to disease progression. This comprehensive

approach is also making strides in infectious disease research

by helping us better understand how pathogens interact with

their hosts and identifying key molecular targets for vaccines

and treatments.

In the realm of drug discovery, multi-omics is enabling the

development of more detailed models of disease pathways,

leading to the identification of new drug targets. This integrated approach not only accelerates drug development but

also supports drug repurposing by revealing new uses for

existing compounds based on shared molecular mechanisms.

Moreover, multi-omics research is driving the creation of

next-generation diagnostics. Non-invasive tests like liquid

biopsies, which analyze circulating tumor DNA, proteins,

and metabolites, are emerging as powerful tools for more

precise disease detection and monitoring.

The future of multi-omics is bright, with ongoing innovations in AI, single-cell analysis, and real-time monitoring.

These advances will continue to improve our understanding

of biology and disease, accelerating the development of new

treatments and diagnostic tools that could transform personalized medicine.

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New Opportunities in Proteomics

A snapshot of technologies like single-molecule protein sequencing and their

impact on proteomics research accessibility

By Maria Rosales Gerpe, PhD

The term proteomics turned 30 in 2024. The concept of a

more complex protein biochemistry can be traced back to

the mid-1970s with the breakthrough of 2D gel analysis. Or

even earlier: in 1958, Frederick Sanger, colloquially known

as the father of genomics, accepted his first Nobel Prize for

sequencing the first molecule—insulin, a protein.

Despite a protein molecule being the first to be sequenced, it

was Sanger’s DNA sequencing—his second Nobel Prize—

that went on to permanently alter the biological sciences,

ushering scientists into the genomics era in the late 1970s.

Proteomics was coined to denote that genomics alone could

not be used to fully explain biological processes. While the

genomics revolution took on exponential growth since the

late 1970s, proteomics has been slow to churn due to inaccessibility, technical challenges, and cost—big hurdles for the

average biomedical lab looking to venture into proteomics.

Though challenges persist, new protein sequencing technologies are emerging with the potential to meet these challenges, and perhaps open a door to mainstream proteomics.

Challenges in obtaining the protein

sequence

Proteins are vital to our understanding of biological function

and disease. But a protein’s structure is complex, the result of

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the odd mutation, frameshifts, alternative splicing, somatic

recombination, post-transcriptional regulation, post-translational modifications (PTMs), proteolytic cleavages, and

more—all of which are vital to a protein’s function.

DNA sequencing data alone cannot predict the final protein

structure, or its expected quantity; however, direct access

to the protein sequence can reveal whether the protein is

expressed from the expected reading frame, whether it is the

result of a frameshift, whether it needs to be modified to be

biologically active, and much more.

The main methods currently used to identify proteins in

proteomics are immunoassays, mass spectrometry (MS), and

Edman degradation. Accessing the protein sequence through

these methods has been difficult due to technical constraints

and inaccessibility.

For instance, because proteins are not replicated—they

are made ad hoc—it can be difficult to detect them, unlike

genes, which can be amplified for detection and quantification. Some researchers highlight that proteins’ limited

bioavailability may be a reason why 10 percent of the human

proteome remains unelucidated, despite knowledge of the

human genome.

Large, purified quantities of a protein are typically required

for MS and Edman degradation analysis. Another reason is

that some proteins may be resistant to chemical or enzymatic

manipulation; trypsin is often the enzyme of choice for MS

analysis, but not all proteins are sensitive to its proteolysis.

Many more enzymes are now used in MS-based protein

sequencing, but this results in complex datasets that require

sophisticated algorithms for analysis. Unelucidated proteins and proteoforms could also be poor binders, avoiding

detection.

Similar to enzymatic resistance, antibodies may not bind

proteins during modified states, such as activation through

phosphorylation. 85 percent of proteins are not suitable for

targeting by small-molecule binders or antibodies, earning

them the moniker of the “undruggable proteome.”

Furthermore, antibodies’ specificity and reproducibility

are not always consistent, requiring extensive validation.

Non-specific signaling and incorrect target binding are not

uncommon for immunoassays, despite topping the list of

accessible methods available for protein identification. Researchers can readily learn and perform immunoassays with

little cost compared to MS.

Relatively accessible to non-proteomics researchers, Edman degradation, which involves the sequential cleavage of

a protein via its exposed N-terminus, is limited by its low

throughput and chemical incompatibility to certain PTMs

that block the N-terminus.

Least accessible to the average biomedical researcher, MS,

which measures the mass-to-charge ratio of ionized peptides, must be carried out by highly trained personnel. Plus,

instruments like mass spectrometers tend to occupy significant space in labs, in contrast to DNA sequencing instru-

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ments that can fit snugly in the corner of a bench. MS is also

expensive to conduct routinely.

New technologies and innovations

making an impact in proteomics

In recent years, scientists have sought inspiration from DNA

sequencing by seeking ways to boost the signal of individual

peptides and enable single-molecule sequencing without

relying on prior knowledge of the genetic code.

Examples include Schaus et al. (2017)’s DNA nanoscope

or DNA proximity recording that localizes and identifies

specific amino acids through amplification of proximal

DNA-barcoded probes with complementary primers, and

nanopores—membrane pore that only permits single-file

flow of molecules. But, like DNA proximity recording,

“nanopore-based protein sensing is still in its infancy,” reads

a 2021 Nature Methods’ review.

Recent innovative alternatives to Edman sequencing—

fluorosequencing and N-terminus amino acid binding

(NAAB)—may be more suitable for mainstream adoption

of protein identification, allowing diverse research groups

to contribute to proteomics. Of the two, fluorosequencing’s

complex chemistry requirements may incur problems such

as chemical destruction of dyes, not reported with NAAB

single-molecule protein sequencing technology.

The latter relies on NAAB proteins, which bind to N-terminal amino acids, and can be fine-tuned in specificity and

affinity to different amino acids through directed evolution (yeast or phage display), already accessible in many

molecular biology labs around the world, and much more

cost-effective.

NAAB-based single-molecule protein sequencing involves

binding C-terminus immobilized proteins or peptides to single amino acid-specific NAAB dye-labeled probes before or

while enzymatic stepwise peptide cleavage occurs, revealing

additional N-termini for several cycles until a sequence of

the peptide is identified.

In 2022, a paper published in Science showed that immobilizing peptides into a semiconductor chip for NAAB-based

single molecule protein sequencing could be successfully

used to generate unique “fluorescence properties and pulsing

kinetics” signatures for each amino acid. The distinct signals

could then be used to train software to identify amino acids

as fluorescence and kinetics were recorded per well in the

semiconductor chip.

The latter study also used directed evolution to discover

additional NAABs, which they dubbed “recognizers,” from

ClpS aminopeptidases and UBR family of ubiquitin ligases,

showing that specificity and sensitivity can be optimized for

single-molecule detection. This type of sensitivity is not yet

found in MS-based protein sequencing.

Conclusion

It’s no coincidence that the first molecule to be sequenced

was a protein, as they are vital to our understanding of biological function and disease. But for many years, the average

research lab has mostly used immunoassays to study them,

resorting, when affordable, to MS.

The use of Edman sequencers comes with limitations such as

low throughput problems and other technical issues. Recent

innovations, such as single-molecule protein sequencing

through NAAB proteins, may pave the way to mainstream

adoption of protein sequencing by offering high-throughput capabilities and higher specificity not possible through

Edman sequencing or MS.

“Recent innovative alternatives

to Edman sequencing—

fluorosequencing and N-terminus

amino acid binding (NAAB)—may

be more suitable for mainstream

adoption of protein identification,

allowing diverse research groups

to contribute to proteomics.”