Categorizing Cells with Machine Learning and Latent Space

Two exciting and complementary machine learning methods for assigning cell identity based on single-cell sequencing data were published in a paper from Johns Hopkins. The first program, scCoGAPS, defines latent spaces from a single-cell RNA-sequencing dataset to categorize cells and the second program, projectR, evaluates latent spaces in independent target datasets using transfer learning. These two methods are interesting advances towards a goal that is likely still far off—understanding exactly what makes each cell what it is. For an excellent summary read the press release, Finding A Cell’s True Identity.

The original article is a more complicated reading but interesting through out.

Stein-O’Brien, et al. (2019) Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. Cell Systems

Summary

Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.

Advances in Single Cell RNA-Sequencing

Single cell transcriptomics: A new sequencing approach

“Researchers from University of Southern Denmark, Wellcome Sanger Institute and BGI, today published a study in the journal Genome Biology comparing the library preparation and sequencing platforms for single-cell RNA-sequencing (scRNA-seq).

Single cell transcriptomics (i.e. scRNA-seq) is a next-generation sequencing approach that simultaneously measures the messenger RNA concentrations (encoded by DNA/genome/genetic blueprint) of thousands of genes, in individual cells. This enables researchers to gain a high-resolution view of cells to unravel heterogenous cell populations and better understand individual cell functions in the body. Although several single-cell protocols exist, the sequencing has traditionally been performed using Illumina technology and sequencing platforms.”

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Thank you for reading!

Targeting Brain Tumors with Single-Cell RNA-seq

Brain Tumors Through the Single-Cell RNA Sequencing Lens: Researcher Interview with Mario Suvà

Read Peggy Wang’s interview with Mario Suvà for the National Cancer Institute. Dr. Suvà is an assistant professor of pathology at Massachusetts General Hospital and Harvard Medical School, an Institute Member at the Broad Institute, and uses single-cell RNA sequencing as a discovery tool for understanding brain cancer. Lean more about his work and this powerful new approach to understanding this important disease…

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Cell population mapping from bulk single-cell RNA data

Cell composition analysis of bulk genomics using single-cell data

Amit Frishberg, Naama Peshes-Yaloz, Ofir Cohn, Diana Rosentul, Yael Steuerman, Liran Valadarsky, Gal Yankovitz, Michal Mandelboim, Fuad A. Iraqi, Ido Amit, Lior Mayo, Eran Bacharach, & Irit Gat-Viks

Nature Methods (Research Article)

Abstract—Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data (‘scBio’ CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.