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Next-generation sequencing (NGS) is continuously evolving. Traditional short-read DNA sequencing has encouraged break-throughs at ever-lower costs across the field of genomics. However, tumors, brains, the immune system, and other complex systems require the greater resolution and flexibility only recently introduced with single-cell sequencing. Compared to the more established bulk RNA sequencing (RNA-Seq), single-cell sequencing magnifies cellular differences to glean intel about how an individual cell functions in its environment. By sequencing individual cells to determine the base sequences, researchers can obtain genomic, transcriptomic, or multiomic data on a cell-by-cell basis, revealing details that are otherwise overlooked.

Single-cell sequencing applies to any study that requires detailed understanding of a cell population, marking tremendous potential for multiple research areas spanning a diversity of applications. This eBook highlights single-cell innovations and approaches, demonstrating how NGS identifies a neuroblastoma target and provides insights for brain rejuvenation and the development of combination therapies. We also invite researchers to explore how the Element AVITI System synthesizes quality, flexibility, and compatibility to deliver affordable single-cell sequencing at any scale so that you can own the next breakthrough.

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“Next-generation Sample Preparation for Next-generation Applications” explores the novel and automated Laminar Wash system, the only suspension-cell sample preparation workstation designed to eliminate the upstream in-process variabilities caused by the traditional sample preparation method, including labor intensive and hands-on workflows, inconsistencies across users and locations, difficult and time-consuming technology operations, and centrifuge-based mechanical stress on the samples.

Readers will discover the benefits of the Laminar Wash system, including increased efficiency, reproducibility, and workflow optimization. The eBook explores the gentle Laminar Wash technology and the advanced user-friendly software of the system that automate tedious and error-prone manual sample preparation steps. In addition, the eBook discusses the broader implications of this technology for scientific research and analysis in a variety of fields (cell and gene therapy, single-cell multiomics, biomarker discovery, and tumor microenvironment, to name a few). It showcases real-world case studies and success stories, highlighting how automating sample preparation workflows with the Laminar Wash system revolutionizes data quality, throughput, and experimental precision.

Overall, this eBook aims to empower scientists and researchers with a comprehensive understanding of the Laminar Wash system and its potential impact on their scientific endeavors. By embracing this innovative method, researchers can enhance the accuracy, efficiency, and reliability of their sample processing, leading to accelerated discoveries, improved analytical capabilities, and advancements in various fields of science.

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RNA technology has had its own Big Bang event. It occurred just a couple of years ago when, seemingly out of nothing, the mRNA-based COVID-19 vaccines appeared. Well, they only seemed to come out of nothing. The underlying technology for the vaccines had been in development for decades. Nonetheless, under the pressure of the COVID-19 pandemic, the development of mRNA-based vaccines was greatly accelerated. Today, everything in the RNA technology universe is expanding. Production technologies such as in vitro transcription are maturing, RNA modalities beyond mRNA are emerging, and delivery vehicles are proliferating. Most important: like the cosmos, the RNA universe is becoming more interesting. Whereas the cosmos forms galaxies, the RNA universe gives rise to myriad applications. These include vaccines against varied diseases, and therapeutics for conditions from enzyme deficiencies to cancer.

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RNA therapeutics comprise a rapidly expanding category of drugs that will change the standard of care for many diseases and actualize personalized medicine. These drugs are cost effective, relatively simple to manufacture, and can target previously undruggable pathways.

Oligonucleotide therapies include short, single- or double-stranded DNA, or RNA molecules that bind via Watson-Crick base pairing to enhance or repress the expression of target RNA to treat or manage a wide range of diseases. They include antisense oligonucleotides (ASOs), RNA interference (RNAi), and aptamers.

In general, oligonucleotide therapeutics interfere or inhibit the RNA translational process within the cell and promote degradation of proteins associated with disease.

Oligonucleotide therapeutics development and manufacture requires confirmation of the correct nucleic acid sequence and comprehensive characterization and quantification of impurities to ensure therapeutic efficacy. In the later stages of development, this must be done in accordance with strict regulatory guidance. Presently, the guidelines themselves are evolving. The main challenge is presented by the complexity of the oligonucleotide molecules themselves and the modifications that are being applied to them.

Chemical modifications have been utilized extensively to improve the binding energy, stability, and tolerability of oligonucleotide therapeutics, including ASOs, small interfering siRNAs, and aptamers. Phosphorothioate modifications, which incorporate sulfur in the oligo backbone, have been effective due to their ability to enhance cellular uptake and evade nuclease-mediated degradation in serum.

Chemical modification continues to evolve and bring more drug-like properties to oligonucleotide therapeutics. These include sugar and base modification and/or conjugated molecules that improve target delivery. Next-generation analytical solutions from Thermo Fisher Scientific are evolving, too. Our chromatography and mass spectrometry solutions allow you to rapidly characterize and quantify components of your oligonucleotide therapeutics with greater clarity and accelerate the progression to market.

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Next-generation sequencing (NGS) has revolutionized life science and clinical research in numerous ways. Before the advent of NGS, traditional sequencing methods were time- consuming, expensive, and could only sequence a small fraction of the genome. With the development of NGS, it is now possible to sequence the entire genome of an organism in a matter of days, at a fraction of the cost of previous methods. This has led to a wealth of new information being generated about genetic variation, disease-causing mutations, and other biological phenomena. In clinical research, NGS has allowed for the study of the genetic basis of diseases in unprecedented detail, resulting in new treatments and therapies.

Library preparation is a crucial step in the NGS workflow, involving the preparation of DNA or RNA samples for sequencing. Automated library preparation solutions streamline this process by using robotics to perform the required steps, reducing the potential for human error and increasing repro-ducibility as well as the hands-on time required for library preparation, freeing up researchers to focus on downstream data analysis and driving faster go-to-market. Automated library preparation solutions are available from a variety of vendors and can be tailored to specific research needs. For example, some solutions may be optimized for low-input DNA or RNA samples, while others may be optimized for specific sequencing applications, such as whole-genome sequencing or targeted sequencing. In this eBook, you will learn about how to unlock the power of genomics with automated NGS library preparation solutions.

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Sensitive and specific biomarkers can help accurately identify complex diseases early as well as provide clinicians with information useful for directing treatment, identifying disease endotypes, stratifying patients for clinical trials and predicting patient outcomes, including prognosis and adverse responses. An ideal biomarker is accessible, easy to measure, specific to the disease, and reproducible.  Genomics and proteomics have historically produced valuable biomarkers.  Genomics research has uncovered mutations with propensity for disease, such as BRCA1, while proteomics research has identified disease associated proteins, such as Tau protein and beta-amyloid (this issue chapter 4) in Alzheimer’s. Both are helpful in studying disease and determining patient care, but lacking in early detection and prognostic value where the ideal biomarker may considerably improve patient outlook. Recent technological advances in microarray and machine learning technologies have made it possible for scientists to take advantage of patient antibody signatures.

Antibodies are ideal biomarkers because they are manifestations of the actual disease, occurring early, before symptoms, and persisting through the duration of the disease. Antibodies are highly specific, easy to obtain and measure and can represent different aspects of disease, enabling a mechanistic view of disease pathophysiology. New high throughput technologies including protein arrays and machine learning have enabled researchers to identify antibody signatures, or panels of disease associated antibodies, with unprecedented insight.  Signatures have much greater diagnostic and prognostic potential than single proteins or gene mutations (Bizzaro, 2007; Kathrikolly et al., 2022).  In particular, an individual’s immunosignature is emerging as one of the most sensitive, accurate and predictive sources of disease pathophysiology.  Immunoprofiling is not new, but the ability to quantify thousands of antibodies from a small blood sample with protein microarrays provides a new unheralded level of patient detail. A new generation of biomarkers is emerging, capitalizing on the convergence of high throughput measurement techniques, advanced bioinformatics and new machine learning models. In this eBook, we review biomarker discovery, emphasizing immunoprofiling with autoantibody signatures, that once deconvoluted with machine learning, can provide insights into disease progression, potential treatment adverse effects and mechanistic information about disease physiology.

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In recent years, genomics advances have dramatically improved our understanding of the human genome and the genetic basis of disease. This knowledge has enabled us to develop new treatments, diagnostic tools, and preventive measures that can improve patient outcomes and quality of life. Perhaps most exciting are new opportunities for improving collaboration between clinicians, researchers, and data scientists as they work together to understand biological pathways and disease progression.

Today, genomic testing is only one piece of the puzzle; data analysis techniques are essential for interpreting results. By leveraging various algorithms, including artificial intelligence (AI) models embedded in data analysis pipelines, clinicians can quickly sift through massive datasets, looking for patterns and insights that would otherwise be difficult to detect manually. This approach has already yielded promising results in cancer therapy, where pipelines leveraging AI-based approaches can help identify tumor markers or predict drug responses.

For precision medicine initiatives, collaborative data analysis techniques are becoming critical. New software approaches enable researchers from different disciplines (such as genetics, computer science, and epidemiology) to collaborate more effectively and securely to optimize patient outcomes by combining their expertise and data sets.

In this eBook from GEN, sponsored by Seqera Labs, we delve into collaborative data analysis pipelines from multiple perspectives. We explain how next-generation pipeline management coupled with effective use of cloud infrastructure can help boost productivity, enable deeper analysis, and even reduce costs. Technologies such as Nextflow, Nextflow Tower, and high-quality curated community pipelines are changing how organizations view genomic data analysis. Today, Nextflow-powered pipelines are used for everything from basic research to genetic surveillance to personalized medicine.

As these fields continue to evolve together, we will see new scientific breakthroughs and improvements in how healthcare providers diagnose and treat their patients — outcomes that are good for all of us. So read on, and learn how modern collaborative data analysis techniques are helping to untangle some of the most challenging problems in biology.

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Recent advances in analytical techniques have increased the efficiency of therapeutic antibody discovery and facilitated rapid growth of the mono-clonal antibody (mAb) market over the past 25 years. There are now over 100 FDA approved mAb therapeutics, with the vast majority indicated for treatment of cancers, for example Rituximab treatment of non-Hodgkin lymphoma, and immune disorders, such as Humira treatment of rheumatoid arthritis¹.

Next generation mAbs utilize the backbone of existing therapeutics with modifications to improve characteristics, such as potency or specificity. These can be categorized into four main groups based on the modifications they include². The first group have engineered Fc regions to enhance function and/or half-life. The second have adaptations to improve specificity, including bi- and tri-specific antibodies. The third group are fragmented antibodies such as nanobodies, with greater stability and tissue penetration compared to native immunoglobulins³. The final category are conjugated antibodies, which includes antibody-drug conjugates.

Conventional methods for characterizing antibody binding and function, such as ELISA or traditional flow cytometry, can suffer from limited throughput, large sample volume requirements and the need to combine results from multiple instruments. The iQue^® Advanced Flow Cytometry Platform, with validated assays and reagent kits, provides a streamlined solution for high-throughput antibody screening assays. Low volume samples are rapidly acquired from 96- or 384-well plates, then processed in the integrated iQue Forecyt^® software, with auto-compensation and simple data analysis to deliver fast, actionable results.

This eBook highlights some of the most recent advancements in the field of antibody discovery, covering a range of methods of antibody characterization and the novel modalities produced. One article outlines work from scientists at The University of California, who used artificial intelligence to predict binding affinity from antibody sequence. Another article describes a study from the University of Southampton, which suggests fine-tuning antibody binding affinity may result in improved efficacy, with some lower affinity immunomodulatory antibodies mediating greater signalling. The included application notes from Sartorius detail in vitro assays that use the iQue^® platform for functionally profiling antibodies. This collection of articles outlines the use of flow cytometry as a critical tool to advance antibody discovery.

References

1. H. Kaplon, A. Chenoweth, S. Crescioli & J. M. Reichert, Antibodies to watch in 2022, mAbs, 14:1 2022.
2. C. Challener, Witnessing Major Growth in Next-Generation Antibodies, BioPharm International, 30 (4) 2017.
3. S. Sun et al., Nanobody: A Small Antibody with Big Implications for Tumor Therapeutic Strategy, Int J Nanomedicine, 22;16:2337-2356. 2021

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The development of antibodies for diagnostics or therapeutics requires comprehensive characterization of affinity, specificity, and mechanism of action (doi.org/10.1016/j.xpro.2021.100836). Affinity measurements screen different isolates of an antibody to identify those that are most effective at binding antigen. Also, measurements of nonspecific binding to compounds that are structurally related to the antigen of interest can help establish the likelihood of cross reactivity with other molecules that accompany the antigen.

Biolayer interferometry (BLI) is a label free technique that measures the interference pattern of white light reflected from the surface of a biosensor, which indicates the presence of biomolecular interactions. The binding between a ligand immobilized on the biosensor tip and an analyte in solution produces an increase in optical thickness at the biosensor tip, resulting in a wavelength shift proportional to the extent of binding (Azmiri and Lee,  2015; Mechaly et al., 2016). The sensor tips collect readings in real time, while immersed in the analyte solution, without the need for continuous flow fluidics (Yang et al., 2017).

BLI technology is widely used for quantitation of antibodies, which is fundamental to biological research and production processes. The technology is also extensively used for kinetics measurements of antibodies and small molecules to assess the strength and speed of binding of an antibody to a target. This is possible due to BLI’s robustness to complex matrices, speed, accuracy, and ease of use.

Biosensors with different ligands that bind antibodies, proteins and small molecules such as  streptavidin, human Fc, mouse Fc, protein A, anti-his, and Ni-NTA are routinely used. However, many of the first generation BLI biosensors have limited dynamic range, poor small molecule binding, and higher costs due to single use operation. The wider adoption of this simple and yet powerful technology was also limited by relatively expensive instrumentation and complex software.

This e-Book explores advances in biosensor technology and instrumentation that can significantly accelerate antibody discovery through efficient quantitation, kinetics, and epitope binning.

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Modifications and adaptations continue to improve how we use PCR, spurring a new level of creativity in genomics hypotheses and enabling a broad versatility in applications. PCR has the speed, sensitivity and reliability to assume an ever-expanding role in a wide range of basic, translational and clinical research approaches. Employing tools like microfluidics has further simplified and accelerated genomic analysis using PCR, with the ability to run thousands of reactions in one chip.

For biobanking endeavors, this empowers a rapid and confident test for sample identification, quality control and assessment. It is a universally recognized priority to ensure accurate sample tracking and quality acquisition, typically done using DNA fingerprinting. This methodology can extend from high-throughput genomics centers to biorepositories or centralized cell line banks.

In pharmacogenomics (PGx), the study of how genes affect individual response to drugs, tools capable of quickly and easily investigating the relationship between a specific gene profile and a novel drug can improve efficacy and shorten the time to research insights. Advances to real-time PCR using microfluidics technology facilitate PGx studies with a single high-throughput workflow that consolidates multiple assays into one experiment, without the  need for multiplexing.

Tumor gene expression has proven to be an effective measure of immune response in cancer progression and therapeutic response research. As emerging therapies reveal new biomarkers and expand the need for samples, the costs and labor required to complete this important work increase, necessitating sensitive and cost-effective toolsets for identifying gene expression signatures from immune and cancer cells.

In this eBook, we examine challenges and solutions in PCR and real-time PCR assay design and highlight the expanding use of microfluidics-based PCR.

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