Use Cases

A platform so flexible you can run any data, any analysis, for any type of science.

Library Screens

Library based, high throughput drug design

Many biologic therapeutic modalities are easy to synthesize in combinatorial libraries, including antibodies, RNA binding therapies, viral vectors, and more. Using these methods with high-throughput library screening, biotechs are able to evaluate the fitness of hundreds of thousands of potential drug candidates against different targets or other types of selection.

diagram showing the steps of the this use case

Manage NGS experiments

Biotechs often use sequencing to count performant library members after an enrichment assay, resulting in large FASTQ files. These files are difficult to share, move around, and explore.

Here a bioinformatician populates Latch Data. They may upload files from AWS s3, BaseSpace, FTP, SRA, or local computers. They may do this from the comfort of their terminal. is a cloud native filesystem that can store and version all of your sequencing files.

A biologist can then easily access, QC, organize, and explore their sequencing data. Scientists do not need anyone else to do this for them after it has uploaded.

screenshot closeup of latch data filesscreenshot closeup of latch data files

Identify Enriched Library Members

Pooled library screens allow for hundreds, or even thousands, of candidate genes to be screened simultaneously. A bulk measurement is then taken from the pooled sequencing product, and computationally untangled to reveal which candidates are enriched or depleted in the treatment group.

Here a bioinformatician can use the Latch SDK to create production-grade QC and barcode counting workflows to process library members from raw sequencing files. They can easily iterate on the parameters and errors with biologists with full control over code.

screenshot of an example panning enrichment workflow parameter interface

Here as a biologist, thank your bioinformatician. You now have a simple interface to analyze your enrichment data yourself.


Compare Library Performance across Conditions

When working with large-scale library screens under different selection conditions, it can be challenging to choose a “winner” given experimental variability. Statistical analysis is used to guide further investigation by narrowing in on lead candidates.

screenshot of an example comparison graphs

A bioinformatician can use Latch Pods to write flexible, custom jupyter or Rstudio notebooks to plot enrichment counts. Build an application to compile your results and expose it directly to your scientists without worrying about hosting or infrastructure.

The biologist can then easily interact with plots, allowing them to change filters and parameters on custom-built applications. For example, an application may display top performing library members from multiple enrichment experiments.


Organize Enrichment Data for Longitudinal Analysis

When evaluating libraries of candidate therapeutics against numerous conditions, the complexity of the associated sequencing data can quickly become unwieldy. It is critical to clearly register your past experimental data in an organized manner, to enable searching of previous results, contextualization of new data, and synthesis of new conclusions.

Latch Registry is like Notion or Google Sheets but designed for sample sheets. Registry lets you impose structure and organization on files, associating experimental inputs and outputs with metadata.

screenshot of an example registry data organization

A bioinformatician team can build type-validated tables in Registry The bioinformatics are able to enforce accurate data entry, easily clean tables in code, and ingest them into bioinformatics workflows for batch processing.

The biologist can then go in and fill in the samplesheet metadata in a single place. They are easily able to view past experiments in a table that displays both experimental metadata and files in single location.

Spatial Epigenetics

Exploring Epigenetic Signatures with Spatial Resolution

Assays that profile epigenetic modifications are important to understand gene regulation and function. Spatial resolution of these epigenetic signatures provide distributions of these biochemical modifications in their native tissue context and are becoming important tools to elucidate how the geometry of epigenetic changes affect the biology of disease or development [1].

example of the spacial imagery

AtlasXomics pioneered DBiT-seq (deterministic barcoding in tissue for spatial omics sequencing), a powerful technique to add spatial context to a variety of NGS assays. Through the utilization of microfluidic chips, DBiT-seq directs barcodes to tissue sections with precise coordinates. In particular, DBiT-seq can be used in conjunction with ATAC-seq, to provide a spatially resolved chromatin accessibility map[2].

When scaling up their analysis for customers, challenges arose — synthesizing tissue anatomy with epigenetic state relies on techniques in image processing and sequencing bioinformatics that involve computation-intensive analyses.

How AtlasXomics leverages Latch to analyze & deliver spatial ATAC-seq and CUT&Tag data for customers

diagram showing the steps of this use case

Scientists run AtlasXomics’ Novel DBiT-seq Assay to Obtain Spatial Context for NGS Data.

AtlasXomics provides the DBiT-seq assay, which is the first platform to enable users to profile the epigenome with spatial context at the cellular level, building upon insight generated by single-cell ATAC-seq [2] and CUT&Tag [6]. Spatial epigenomics can uncover the causative relationships between the chromatin state or histone modifications and the context-specific gene expression of cells within tissue.

This assay combines high-quality imaging with advanced spatial barcoding techniques to locate their ATAC-seq data in tissue.

diagram showing library prep steps for the DBiT-seq assay

AtlasXomics provides services or kits for customers to process tissue samples with the DBiT-seq assay. The processing of these produces hundreds of gigabytes of FASTQ files stored on the Illumina BaseSpace platform, as well as high-resolution images.

latch platform data tab screenshotlatch platform BaseSpace importer screenshot

Using Latch’s native BaseSpace integration, these sequencing datasets can be easily imported into the AtlasXomics workspace and stored alongside all of the imaging and supplementary files.


AtlasXomics hosts their AtlasXBrowser through Latch to enable customers to perform image preprocessing

After the initial assay data has been collected, their customers must perform some basic preprocessing on the high-resolution images taken on the spatial samples. AtlasXomics created the AtlasXBrowser to allow customers to assign epigenetic sequencing data to the correct location in its original tissue.

screenshot of atlasxbrowser

This web application guides scientists through basic image processing, allowing them to crop and designate the Region of Interest (ROI) and threshold to distinguish between on-tissue and off-tissue regions, and generate the spatially resolved barcodes needed to assign sequencing reads to image coordinates in downstream steps.

AtlasXomics’ bioinformaticians host this application using a Latch Pod, a flexible cloud-hosted computing environment. The Latch Pod is then published and hosted on a public link which can be accessed by AtlasXomics’ customers.


Scientists run a preprocessing NGS workflow to generate fragment files from raw sequencing data.

The spatial epigenomics assays generate large files of barcoded sequencing reads, each of which corresponds to a unique transposase insertion event at the sequenced locus [4]. These reads must be aligned to a reference genome, their barcodes error corrected, their adapters trimmed and aligned coordinates adjusted for known behaviors of the transposase (Tn5-shift) [5]. The result is a fragment file, annotating sequenced fragments with genomic coordinates and error-corrected barcodes.

screenshot of atac seq preprocessing workflow

The bioinformatics workflow for pre-processing ATAC-seq data requires computers with large resource profiles, an environment with the proper dependencies setup, a system to move and track the large input and output files to and from the computer running this code. Additionally, the pipeline requires command-line expertise and was originally inaccessible to scientists, causing delays to AtlasXOmics’ clients.

Using the Latch SDK, AtlasXomics was able to create a user-friendly and scalable workflow that their scientists can independently run to generate fragment files from raw sequencing data generated from the DBiT-seq assay.


Finalizing Results for Delivery to Clients

After fragment files are generated, they are then packaged into an ArchR project using another workflow. Each package contains epigenetic fragment data paired with precomputed statistics necessary for downstream analyses.

screenshot pod list in Latchscreenshot of analysis in a rstudio environment

The resulting folder is directly pulled into Latch Pod setup with ArchR. A scientist can employ this method to identify distinct cell groups and understand their interactions using unbiased clustering. Additionally, they can detect gene regulatory elements associated with these groups through differential gene, peak, and motif analysis.

screenshot of rShiny app containing ArchR data with spatial graphics

After finishing the analysis in ArchR, AtlasXomics uses Latch to handoff the resulting data and customized ArchR app to their customer. The customer can view all of the results in the platform, explore the ArchR app, and easily egress the data for further analysis in their environment.


Enabling biological discovery

Despite the potential of spatial ATAC-seq and CUT&Tag, widespread adoption of the assays has been limited by computational complexity of data analysis. However, AtlasXomics' microfluidics chip and DBiT-seq method, integrated with LatchBio cloud, democratize this technology for bench scientists, offering high-density, high-resolution molecular insights into tissue epigenetics.

This advance in the DBiT-seq technology is addressing a blind spot to most researchers by providing both spatial and epigenomic information directly from tissue samples. Epigenomic analysis via spatial ATAC-seq or CUT&Tag adds the much-needed spatial context missing from single-cell sequencing applications. Mapping the epigenome is an essential step in understanding regulation in gene expression underlying various disease states. The utilization of spatial epigenomics in translational research will be transformative to developmental biology, neuroscience, and oncology, among others.

Map Genes of Interest

Similar to gene expression levels in a transcriptomic assay, ArchR uses gene activity scores that incorporate accessibility of both the gene body and distal regulatory elements are used to rank genes by epigenetic activity [3]. Combined with spatial information, these scores can be used to visualize the regulatory landscape of a gene of interest in the original tissue context.

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Pictured is a spatial heatmap of the gene activity score for Opalin, a marker gene for oligodendrocyte cells, across three spatial H3K27ac CUT&Tag replicates. H3K27ac is a histone modification associated with increased transcription and is recognized as an active enhancer mark.

Cluster and Identify Cell Types Without Bias

The AtlasXomics analysis pipeline allows for unbiased clustering of cells and neighborhoods by similar epigenetic features. Cell types, along with their location in tissue, can then be identified by associating these groups with well-characterized marker gene(s). Spatial epigenomics can reveal discreet populations of cells and tissue heterogeneity purely through epigenetic signatures.

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Top: Pictured is a spatial map of a mouse hippocampus showing the categorization of each tissue region into distinct Clusters, which loosely show the distribution of putative cell types across the tissue.

Bottom: Map of oligodendrocyte module score based on a combination of associated marker genes (Mbp, Opalin, Mog, Mobp, Cspg4, Cldn11)

Discover Differentially Activated Genes between Clusters

Identifying statistically significant gene activity between clusters of distinct epigenetic signatures can provide a new understanding of the biological mechanisms underlying tissue morphology.

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Pictured above is a clustered heat map of spatially differentiated gene activity scores from spatial CUT&Tag assay which are used to identify putative cell types.

Visualizing Chromatin Accessibility and Histone Modification Genome Tracks

The unbiased nature of spatial ATAC-seq and CUT&Tag allows for researchers to query the epigenetic signatures controlling expression for any gene. The custom app available through Latch helps to visualize local accessibility and histone modifications from pseudo-bulk data between clusters. Synthesis of accessibility and histone modifications around genes of interest can identify differential regulation of promoters, enhancers, and other regulatory elements in each distinct cell population.

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Pictured above is the genome browser track visualizing potential regulatory elements around Olig1 gene (a marker for oligodendrocytes). In spatial ATAC-seq, peaks represent open chromatin. In spatial CUT&Tag, peaks represent genome regions with a specific histone modification.

Differential Regulatory Landscape by Cell Clusters

Researchers can identify unique cell epigenetic signatures by clustering and distribution of accessible regulatory elements. This allows for the analysis of widespread changes to the chromatin that impact gene regulation based on a cell cluster’s local environment.

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Top: Pictured above shows the distribution of regulatory elements by distal, promoter, intronic and exonic regions in each cluster.

Bottom: differential regulatory elements associated with each cluster.

Empowering Spatial Epigenomic Analysis

By integrating the revolutionary technology of DBiT-seq and the Latch environment, AtlasXomics and Latch are democratizing spatial epigenomics. All biologists can now access genome-wide spatial epigenomics insights and push the boundaries for omics analyses in their respective fields.

  1. Vandereyken, K., Sifrim, A., Thienpont, B. et al. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 24, 494–515 (2023).
  2. Yanxiang Deng et al.,Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level.Science375,681-686(2022).DOI:10.1126/science.abg7216
  3. Granja, J.M., Corces, M.R., Pierce, S.E. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet 53, 403–411 (2021).
  4. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol. 2015 Jan 5;109:21.29.1-21.29.9. doi: 10.1002/0471142727.mb2129s109. PMID: 25559105; PMCID: PMC4374986.
  5. Zhang, H., Song, L., Wang, X., Cheng, H., Wang, C., Meyer, C. A., ... & Li, H. (2021). Fast alignment and preprocessing of chromatin profiles with Chromap. Nature communications, 12(1), 1-6.
  6. Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., and Fan, R. (2022). Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science 375, 681-686. 10.1126/science.abg7216.
  7. Bale, T.L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nature Reviews Neuroscience 16, 332-344. 10.1038/nrn3818.
  8. Zheng, Y., Habes, M., Gonzales, M., Pomponio, R., Nasrallah, I., Khan, S., Vaughan, D.E., Davatzikos, C., Seshadri, S., Launer, L., et al. (2022). Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study. Aging (Albany NY) 14, 1691-1712. 10.18632/aging.203918. 
  9. Lu, Y., Chan, Y.-T., Tan, H.-Y., Li, S., Wang, N., and Feng, Y. (2020). Epigenetic regulation in human cancer: the potential role of epi-drug in cancer therapy. Molecular Cancer 19, 79. 10.1186/s12943-020-01197-3. 
  10. Yang, J.-H., Hayano, M., Griffin, P.T., Amorim, J.A., Bonkowski, M.S., Apostolides, J.K., Salfati, E.L., Blanchette, M., Munding, E.M., Bhakta, M., et al. (2023). Loss of epigenetic information as a cause of mammalian aging. Cell 186, 305-326.e327.


Metagenomics Classification

Next-generation sequencing, specifically ‘metagenomics’, has enabled a fundamentally new understanding of the microbial world. The gut microbiome has been implicated in a range of health outcomes from obesity, to Alzheimer’s, to immunotherapy response. R&D groups monitoring bacterial species diversity and discovering microbiome-derived therapeutic candidates need access to large-scale data analysis capabilities from day one.

diagram showing the steps of the this use case

Manage all metagenomics data

The monitoring of microbial samples over time generates large collections of FastQ files that will only grow in number. Storage and processing infrastructure must flex accordingly.

screenshot closeup of latch data filesscreenshot closeup of a latch registry table

Latch allow your team monitor your microbial samples as large collections of FastQ files within latch. Import them to Latch Data and then organize/associate them with metadata in Latch Registry


Obtain Amplicon Sequence Variants (ASVs) to find sample composition

Today’s metagenomics sequencing technologies generate a variety of read lengths and outputs. You must stitch reads together and assemble them into genes or organisms. Complex multi-step bioinformatics pipelines are needed for insights like variant identification.

screenshot closeup of parameters on a dada classification workflow

A bioinformatician can use the Latch SDK to develop a variant identification workflow with a user-friendly interface for other scientists/biologists to run. Or they may use common metagenomics tools which are available on Latch:


Explore phylogeny and taxonomy. Identify microbial features for more research.

Generate visualizations from taxonomic classification data in your browser. Explore alpha or beta diversity and phylogenetic relationships. All of this in custom, templated notebooks.

abundance diversity graphgraph sequence percentage

A bioinformatician can develop a Latch Template that serve Shiny applications like Phylo-seq for scientists to track the dynamic evolution of a microbial population over time. The biologists on their team simply click “Use Template” to begin their own analysis, without having to set up an environment or install dependencies.

Pod Templates on latch

Elegantly Store Data and Organize for Longitudinal Analyses

Take raw outputs from taxonomic classification pipelines, as well as analysis reports, and attach metadata. Organize samples based on characteristics like time point or Patient ID in Latch Registry.

Pod Templates on latch

Query data to assemble comparisons of certain samples based on time point.

Pod Templates on latch


Immunoreceptor Discovery & Screening

Immunomodulation is an increasingly important strategy for combating cancer, infections, and inflammatory diseases. Fine understanding of immune cell phenotypes and T-/B-cell specificities directly translates to the creation of innovative therapeutics, such as mRNA vaccines and engineered T cells. These processes rely on TCR/BCR complex discovery and characterization. Previously a laborious multi-year endeavor, the process of finding these receptors can now be condensed into a matter of weeks using RNAseq.

Let’s explore a common use case enabled by single-cell genomics: paired ɑ/β TCR sequencing.

diagram showing the steps of the this use case

Derive paired ɑ/β TCR nucleotide sequences from sc-RNAseq assays

Single-cell genomics has allowed for the preservation of paired ɑ/β TCR transcripts, which would otherwise be difficult to obtain from bulk samples. Organizing TCR sequences by themselves, or with associated gene expression files, can be complex.

screenshot closeup of latch data files

A bioinformatician can import all this data easily in Latch Data. And then specific TCR + GEX sequences can be organized intuitively for biologist counterparts using Latch Registry.

screenshot closeup of latch data files

After all of the data has been imported, a biologist can easily find TCR + GEX sequences of interest based on functional annotations and clonotype IDs.


Align V(D)J fragments to germline and annotate clonotypes

Immunoreceptor genes are some of the most difficult to sequence on account of the biological complexity of V(D)J recombination. To begin, full-length transcripts must be obtained at the RNA level, then fragmented into V(D)J regions for alignmentment with the germline contig.

To run the analysis, a biologist can drag and drop V(D)J FastQ files, then launch cellranger vdj to process sequence fragments from 10X and generate clonotype annotations — without any code.

screenshot closeup of latch workflow parameters

Your teams bioinformatician can also leverage the Latch SDK to upload your own custom V(D)J alignment workflow from the command line. Any workflow uploaded through the SDK allows biologists to run the workflow in a no-code GUI. It also seamlessly integrate new program updates, letting Latch auto-version your previous work.

screenshot of the workflow development page

Explore TCR clonality and generate visualizations

The immune repertoire contains incredible diversity of clonotype sequences. Observing TCR/BCR clonality over time can generate important insights into disease states and mechanisms of therapeutic response.

A biologist can explore TCR/BCR clonal dynamics using a public Pod Templates like Immunarch. View clone size and expansion kinetics over time. Extract TCR sequences of interest.

screenshot of the workflow development pagescreenshot of the workflow development page

A bioinformatician can generate custom R and Python notebooks for scientist colleagues to run independently. Deliver Jupyter Notebook and R Markdown reports to collaborators using Pod Templates .

Protein Engineering

Accelerate Protein Design

Rational design of proteins can aid a multitude of translational R&D efforts. Models like AlphaFold have been used forin silico SARS-CoV-2 antigen predictions, de novo protein design, and characterizing agents of antibiotic resistance. However usage is blocked by access to GPUs and ease of use for scientists. Latch offers efficient GPU access for running hundreds of protein-ML applications.

diagram showing the steps of the this use case

Launch AlphaFold, ColabFold, or custom models to predict 3D protein structure

Predict 3D protein structures using ready-to-run LLMs.

example parameters on alphafold workflow

Anyone can use the Latch Verified AlphaFold or ColabFold workflow to 3D protein structure from amino acid inputs. Define GPU resources for efficient and rapid LLM inference.


Visualize a .pdb on Latch, or import into a Pod for further investigation

Armed with 3D protein structures, infer functional properties of proteins and explore annotations.

example parameters on alphafold workflow

Or anyone familiar with code can run custom downstream analysis in pods to look at molecular dynamics, binding affinities, and other protein attributes using tools like Bio3D.