From developing new single-cell analysis methods to applying them at scale, transcriptomics experts are using Terra to access data, share tools, and accelerate research
“Being able to customize the environment, share, and scale up the computing resources without having to manage any local resources is transformative.”
Brittany A. Goods, PhD, Postdoctoral Fellow
Single-cell analysis is a rapidly evolving field with a thriving culture of innovation and collaboration. The Shalek Lab is working to make resources and data broadly accessible, as well as develop new approaches for analyzing, integrating, and leveraging large-scale datasets across many biological systems. Thanks to Terra’s scalable interactive computing environment, lab members are able to work collaboratively to develop and apply algorithms that can handle transcriptomic data from millions of individual cells.
Brittany A. Goods and Sarah Nyquist in the Shalek Lab used massively parallel single-cell RNA sequencing with the Seq-Well platform to chart the transcriptional landscape of cells isolated from human breast milk over the course of lactation as part of the MIT Milk Study. The first of its kind, this ongoing study is providing deep insight into the immune state of macrophages and promises to answer some long-unanswered questions about the protective benefits of breastmilk for infants.
This growing dataset is an exciting resource that the Shalek lab is making available prior to publication for the benefit of the scientific community. The authors request that those seeking to use the data for any scientific publication please contact Alex Shalek prior.
Research in the Shalek Lab is directed towards the creation and
implementation of new technologies to understand how cells collectively perform systems-level functions in healthy and diseased states. They employ a comprehensive approach, developing innovative methodologies and applying them across multiple systems to empower more mechanistic inquiry and a deeper understanding of the rules that govern ensemble cellular behaviors.
scRNAseq data (Seq-Well) for 6,349 cells collected from 15 study participants, along with metadata describing infant and maternal factors, such as illness status, medications, and vaccines received
One Jupyter Notebook containing environment setup code and one Notebook containing data analysis code using Seurat and related libraries.
This public workspace contains a subset of the MIT Milk Study (anonymized) as well as the two Jupyter Notebooks set up to ingest the data and step through the complete analysis, so anyone can clone the workspace and build on this work.
Single-cell RNA-seq analysis of previously identified mediators of SARS-CoV cellular entry in various tissues
Optimus pipeline used by the Human Cell Atlas project for processing 10x Genomics v2/v3 transcriptome data
Trinity toolkit for efficient and robust de novo reconstruction of transcriptomes from RNA-seq data
Explore tumor single cell RNA-seq data to visualize evidence for copy number variations