Explore an easy way to extend your in-house resources to include integrated single-cell data and analysis

Single-cell sequencing is a powerful technology that offers a focused approach to biomarker and target discovery. If you're working in oncology research, you most likely use it to develop new diagnostic biomarkers or anti-tumor treatments. If you study the immune system, you may use it to detect individual immune cells or to distinguish among different immune cell groups to propose new targets for disease treatment.

Yet, if you work with single-cell sequencing data, you know that beyond the many possibilities lie many challenges. You probably obtain single-cell RNA-seq (scRNA-seq) data from multiple programs and disease indications, and from both publicly and internally generated data. These data are difficult to integrate and align. What's more, the sheer volume of data and noise within each dataset make it extremely challenging to draw meaningful conclusions.

QIAGEN Discovery Bioinformatics Services eases the challenges of working with single-cell sequencing data to quickly and efficiently help answer questions relevant to your research goals. Our team supplements your workforce with our bioinformatics experts and performs analyses tailored to your interests. We offer a range of support, such as building custom pipelines and server solutions, as well as provide hypercare support and training. We take on everything from secondary analysis services to in-depth analysis of biological data.

Need support with content curation? Leave it up to us, whether it's literature, datasets, pathways or a customized integrated 'omics data collection based on your internal data ('internal Land'). We'll perform deep meta-analysis on data collections and take on the bulk data processing. By working with us, you'll save time organizing and visualizing internal pipelines and results so you can focus on validating your hypothesis to more quickly make your next big discovery.

Easily query thousands of single-cell datasets to facilitate hypothesis generation

In addition to our basic services, our custom-built single-cell analysis pipelines incorporate cutting-edge public and/or proprietary bioinformatics tools to identify rare cell types, track cell lineage, infer developmental trajectories and determine inter-cellular interactions of single cells. We do this using data from many single-cell sequencing methods, such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), assay for transposase-accessible chromatin by sequencing (ATAC-seq), single-cell T cell receptor sequencing (scTCR-seq) and single nucleus RNA sequencing (snRNA-seq), among others. Our experts query the OmicSoft Single Cell Land database to answer your specific biological questions, such as:

• How is a particular target gene expressed across cell types?
• How is this target gene expressed across multiple single-cell projects?
• What is the expression signature for a specific disease state?
• What are the different cell types and their abundance across datasets?
• What is the differential gene expression of treatment vs. control samples in a particular cell type (e.g. monocytes)?

Figure 1. Example output showing the abundance of different cell types across datasets.

Let us help you manage the unique challenges of analyzing scRNA-seq data

General challenges of analyzing scRNA-seq data

We design our pipelines to handle your most common scRNA-seq analysis challenges. These challenges include the relatively small number of sequencing reads, the sparsity of data, limited processing power and cell population heterogeneity. Gene expression profiling by scRNA-seq is also inherently noisier than bulk RNA-seq, making effective data analysis more complicated. To address these caveats of working with scRNA-seq data, we:

• Use a customized unified pipeline script to process scRNA-seq datasets and store the data into internal data lakes called 'Lands'
• Perform stringent quality control checks to eliminate data from low-quality cells
• Make batch corrections before performing downstream analysis to avoid incorrect data integration and interpretation due to batch effect, which is another major challenge in scRNA-seq data analysis
• Include normalization steps to correctly interpret the results from scRNA-seq data for cell subpopulation identification and differential expression calling
• Use imputation as a strategy to replace the missing data (dropouts) with substituted values to recover the accurate expression level of all genes
• Employ strategies such as dimensionality reduction or feature selection to deal with the high dimensional nature of scRNA-seq data, which involves thousands of genes and a large number of cells
• Mitigate statistical issues arising from cellular heterogeneity using a set of known markers or use algorithms for unsupervised clustering. We incorporate public and proprietary algorithms in the pipeline, such as Seurat, which can facilitate the identification of cell subpopulations
• Identify distinct subpopulations or groups of cells using differential expression analysis
• Provide a framework to run the samples in parallel using Amazon WorkSpace to store the data

Figure 2. CellMap employing dimension reduction to explore cell types across samples.

Metadata curation challenges

Another challenge of scRNA-seq data is metadata curation. Due to a lack of standards for the deposition of cell-level metadata, you could end up spending the majority of your time processing data and curating metadata. Why not let our team of curators handle the metadata? Our team of experts can manually curate single-cell projects and precisely curate cell clusters to help you more quickly and easily answer your biological questions. To do this, we:

• Extract/curate project, technical, sample and clinical metadata from data submissions and publications
• Apply controlled vocabularies and curation standards
• Curate expected results from papers, such as clustering experiments, cell types and key markers/differentially expressed genes
• Add statistical comparisons using the curated metadata

Differential expression analysis challenges

After identifying the cell type identities of the scRNA-seq clusters, you'd typically perform differential expression analysis between conditions within particular cell types. While commercial algorithms perform differential expression analysis, the p-values from these analyses are often inflated because a cell is treated as a sample. Since single cells within a sample have variation, we can compare gene expression across individual cells. This is known as pseudo-bulk RNA-seq data. Our team implements this approach to create a custom pseudo-bulk integrated 'omics data collection ('Land') so you can easily explore single-cell data to make accurate comparisons.

Figure 3. Explore pre-computed differential expression of marker genes in different cell types, to understand how a target gene differentially expressed in different cell clusters. In this example output, you can see the differential expression of CD34 across different cell types in normal vs. hematologic cancer samples.

Deliverables that get you quick insights

On all our projects, we work with you to determine the output and deliverables that best fit your needs. Here are some examples of our deliverables that'll support your work using single-cell technologies:

• A centralized database that biologists use for disease-focused queries from a customized integrated 'omics data collection based on your internal data ('internal Land')
• Custom scripts enabling the analysis of both human and non-human data
• Support for hypothesis validation by combining information from multiple studies to create a unified picture of biomarkers and disease signatures
• Differential gene expression analysis that assesses changes of a candidate target gene related to a disease
• Signature-based analyses to gain insights into which diseases are relevant for a target gene
• Assessment of relative cell type (cell state) abundance at sample-level resolution for multiple tissue types
• Reports of both differentially expressed genes and gene signatures at cell-type resolution to help you quickly prioritize drug targets, biomarkers and compounds
• Detection of novel cell subpopulations that are responsible for a response to drug treatments (responders vs. resistant cells)
• Identification of subpopulations of cells with variations in gene expression that can provide insight into developmental trajectories (for example, T-helper cell development, B-cell differentiation)

Figure 4. Single-cell project workflow.

Get in touch

By working with QIAGEN Discovery Bioinformatics Services on projects for scRNA-seq processing, you'll save time and increase accuracy to more quickly gain a robust and insightful understanding of complex disease. We'll take on the aspects of your bioinformatics that are most challenging and limiting for you. Our customized support empowers you to more readily gain a robust and insightful understanding of the biological mechanisms in your data, to accelerate and drive your next big discovery.

Would you like to reduce the burden of working with scRNA-seq data to more quickly reach your next biomarker or target discovery? Learn more and request a consultation about our range of bioinformatics services that'll extend and scale your in-house resources. Contact us today at QDIservices@qiagen.com to get your custom single-cell project started. Together, let's unravel the biological discoveries hidden in single cells.

It’s already happened: Several SARS-CoV-2 variants are upon us, lurking in various populations, bringing more uncertainty about how this pandemic will ever get under control.

SARS-CoV-2 is mutating and, in the process, potentially getting more infectious and dangerous, increasing the potential for immune escape and possibly jeopardizing vaccine efficacy. Several geographic variants have recently been discovered. Some have increased transmissibility. Yet for others, it's too early to tell if they are more transmissible, more deadly, will impact vaccine efficacy or escape an already established immunity. Nevertheless, the possibility is there, with every new, significant variant that we discover.

By now, the whole world has learned what 'PCR' means, which merely a year ago few people would have had a clue about. Nevertheless, the virus is getting smarter and there is a growing conviction that relying solely on PCR tests won't cut it any longer. Not now, and certainly not in the future.

Soon, the whole world might know the term 'next-generation sequencing'. With more and more variants emerging and spreading, it is imperative to ramp up genomic surveillance of different variants using whole-genome next-generation sequencing of positive COVID-19 samples – to help avoid further spread locally, nationally and globally.  Public health laboratories, hospitals, sequencing centers and governing authorities/organizations must come together to tackle the challenge of genomic surveillance of SARS-CoV-2.

Along with increased sequencing of positive COVID-19 samples comes the need to scale up operations by laboratories. Every aspect must be scaled, from sample management, sequencing, data analysis, IT infrastructure and results reporting. If you're working in this field and are not already scaling operations, you need to be prepared to do so soon. This could require hiring new people, spending resources on optimizing workflows and time acquiring the bioinformatics and IT expertise necessary to perform data analysis and reporting.

The last, critical part of scaling up your operations is to embrace what's already here and to be ready for what's coming. Labs cannot afford not to be ready. The world cannot afford not to be ready.

The good news is, there is a smarter, more efficient way. We've got a solution that will help.

At QIAGEN Digital Insights, we specialize in bioinformatics software, data analysis, IT infrastructure and interpretation and reporting of results. We have risen to the challenge by developing a fully-automated, constantly-updated, scalable service solution for SARS-CoV-2 genomic surveillance data analysis and reporting of major existing and new variants, with automated quality control and clear visualizations.

With our QIAGEN CoV-2 Insights Service solution, there's no need for extensive IT infrastructure or additional expert bioinformatics personnel. This service automatically processes samples, and within minutes, delivers concise results ready to share with public health partners. It produces a report identifying the lineage of each sample and provides a list of important mutations. Results can also be visualized in QIAGEN CLC Genomics Workbench at no extra cost.  We use a global cloud infrastructure that scales and adapts on-demand, providing the fastest and most secure data processing available.

If there's one thing we've learned so far, it is that SARS-CoV-2 is unpredictable. We must be ready to adapt, work quickly and innovate. To work together and play according to our strengths as individuals, institutions and companies.

Get ahead of the curve! Learn about our QIAGEN Digital Insights SARS-CoV-2 resources and request a consultation about our QIAGEN CoV-2 Insights Service.  Browse our QIAGEN CoV-2 Insights Service resource library, and if you have any questions, don't hesitate to reach out to your local QIAGEN Digital Insights representative at bioinformaticssales@qiagen.com.

Are you a scientist or public health lab professional studying viral host response? Do you find it challenging to find high-quality curated information on gene expression studies from viral host response to advance and deepen your insights and accelerate your research? Or maybe you need a way to combine all the potentially valuable data you find in the public domain but don't know where to start? QIAGEN Discovery Bioinformatics Services is launching a new offering that makes such data readily available with only a few clicks of your mouse. QIAGEN Viral Land represents a highly curated and re-processed collection of high-value human viral host and viral vaccine RNA-seq and microarray expression data, harmonized into a single platform. This dataset represents 201 projects, spanning over 15,000 samples examining viral-host and vaccine research, with the ultimate goal of helping researchers and academic and public health labs generate hypotheses using the wealth of data available in the public domain. This new gene expression database on viral studies leverages a simple-to-use web portal, known as QIAGEN OmicSoft Land Explorer, to:

• Mine public studies, and simultaneously examine existing studies on viral host response to find similar changes between different viruses
• Explore differential gene expression and perform on-demand analyses without the need for time-consuming data processing and sample annotation
• Quickly validate experimental results, and confirm they can be reproduced in similar studies, thanks to custom annotations of study attributes and experimental variables

The new Viral Land delivers web-based access to public gene expression studies. The samples are easily visualized and can be grouped or filtered by sample annotations, such as study type (used for host response or vaccine response), virus classifications, tissue, cell type and more.

The Land Explorer web design allows you to interrogate the expression of one or more genes in a single public study as well as multiple studies that have been processed in the same way.  For example, IL6 is highly expressed in tissues from hosts infected with coronavirus and influenza (Blanco-Melo D. et al.; Figure 1 left). Further examination of a collection of eight viral host studies checking for IL6 expression reveals that IL6 is also highly expressed in blood derived from individuals infected with Zika and dengue relative to uninfected.

Accelerate your antiviral host response research. Request a consultation on Viral Land from our experts today, and visit us here for more information about QIAGEN Discovery Bioinformatics Services.

Reference:

Blanco-Melo D., et al. (2020) Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 181(5):1036-1045.

Need to expand your bioinformatics and curation expertise to harness the power of ‘omics data? Let QIAGEN Discovery Bioinformatics Services help.

Not all research teams have the time and in-house expertise to leverage the power of complex and high-volume ‘omics data, and many organizations lack workforce resources or server and infrastructure support. These limitations create bottlenecks, leaving research goals that don’t advance.

QIAGEN Discovery Bioinformatics Services can reduce the burden of broadening your in-house bioinformatics capabilities. We offer complete Sample to Insight workflows and tools which work together to reveal deep insights into your biological questions. Our team uses best-in-class bioinformatics tools and solutions from QIAGEN Digital Insights to support your projects and goals, whatever your needs may be.

Discover the benefits of partnering with a leading provider of bioinformatics tools and services for discovery research scientists:

• Access the QIAGEN Digital Insights team’s experience and expertise, with 30 years of knowledge and 400 industry experts
• Utilize industry-leading software and knowledge bases
• Leverage our deep customer base and market knowledge
• Over 20 years of trusted data and database security
• Scalable Sample to Insight solutions available from a single trusted partner

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Confronted with challenges to advancing their research, Nofima turned to QIAGEN Discovery Bioinformatics Services for quality results, fast

Dr. Philip James, Ph.D., Senior Researcher at a small research institute called Nofima, was confronted with several challenges associated with access to analytics tools when studying the effect of diet on gene regulation in sea urchins, including processing and pathway analysis of RNA-seq data. He also needed to put the results into a biological context.

Dr. James found partnering with QIAGEN Discovery Bioinformatics Services a reliable and convenient way to extend in-house resources, with our expertise and tailored bioinformatic services ensuring time savings and high-quality results.

Inspirational and groundbreaking science is often unorthodox and tricky, even concerning bioinformatics research. Follow the journey of partnering with QIAGEN Discovery Bioinformatics Services, from initial contact and delivery of data analysis to submission for publication.

Read Nofima's story here.

QIAGEN Discovery Bioinformatics Services offer custom solutions that help translate discovery from your data. This includes tailored plugin development for QIAGEN CLC Genomics Workbench and QIAGEN CLC Genomics Server, helping to extend and customize the feature sets of these NGS analysis solutions.

The QIAGEN Antibody Analysis Suite is one of the custom solutions developed by the QIAGEN Discovery Bioinformatics Services team. This automated, high-throughput tool enables a detailed analysis of antibody sequences and includes the ability to customize to ensure that it fits the exact needs of specific workflows. The Antibody Analysis Suite is used for the characterization of antibody repertoires involved in the discovery and development of therapeutic and diagnostic antibodies and often includes DNA sequence analysis. In vitro display and selection, for example, requires sequence analysis at several steps in the process. First, to test initial library diversity and quality, and subsequently to ensure proper selection and thorough characterization of final clones. The QIAGEN Antibody Analysis Suite provides a tool for detailed analysis that:

• Accelerates the research process
• Removes manual and error-prone steps
• Is user-friendly and integrated
• Can be customized to match specific needs

The Antibody Analysis Suite is a collection of tools that can be fully integrated with the powerful QIAGEN CLC Genomics bioinformatics platform, seamlessly providing endless options for further up- or downstream analysis. The Antibody Analysis Suite can help you:

• Automate and standardize your workflow for heavy and light chain annotation with QC reports
• Build an internal database
• Provide detailed reports and visuals of gene segment usage and mutations with annotation of antibody-specific features
• Automatically annotate antibody heavy and light variable domains and alignments of both DNA and amino acid sequences to proprietary or public databases of germline DNA sequences, or rearranged variable domain sequences
• Understand details regarding V, D and J allele usage and mutations found compared to these; junction sequences; FR and CDR region lengths and sequences, presented in tables and visual displays for further investigation, such as multiple alignments across selected input sequences
• Efficiently identify antibody clones per sample
• Create phylogenetic visualizations and dendrograms
• Perform robust phylogenetic analysis framework that supports metadata-based visualization, as well as sequence clustering
• Analyze results from protein and DNA samples, in parallel
• Determine V, D and J allele usage: Each antibody VH or VL domain is compared to a database of germline sequences of V, D and J alleles, to identify the best hit for each of those gene segments. The alignment of the V segment to its closest germline V segment is displayed in one of many customizable, graphical views
• Perform multiple amino acid alignments: The VH and VL domains of each translated antibody sequence are aligned to a collection of pre-existing, annotated heavy and light domains
• Annotate VH and VL domains: Each sequence is annotated with antibody-specific features, including CDR regions, junction region, specific V, D and J segments and numerous combinations of these
• Quality control your sequences: Each sequence will be checked in terms of frame-shifts and stop codons, as well as missing features, such as conserved amino acids, segments or other motifs
• Group and assemble multiple sequence reads per antibody clone: If an antibody sequence is composed of multiple underlying DNA sequence reads, these can be grouped and assembled prior to analysis
• Create in-house comparisons and databases: Capabilities to make private BLAST databases and protein alignments, such that all analyses can be performed in-house and with proprietary or downloaded public data
• Fully supported to run locally, on servers or cloud environments

Visit us here for more information and request a consultation from our experts today!