Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH

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Abstract

The human cerebral cortex has tremendous cellular diversity. How different cell types are organized in the human cortex and how cellular organization varies across species remain unclear. Here, we performed spatially resolved single-cell profiling of 4,000 genes using multiplexed error-robust FISH (MERFISH), identified >100 transcriptionally distinct cell populations, and generated a molecularly defined and spatially resolved cell atlas of the human middle and superior temporal gyrus. We further explored cell-cell interactions arising from soma contact or proximity in a cell-type-specific manner. Comparison with the mouse cortex showed conservation in the laminar organization of cells and differences in somatic interactions across species. Notably, our data revealed human-specific cell-cell proximity patterns and showed a markedly increased enrichment for interactions between neurons and non-neuronal cells in the human cortex.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Christoph Hafemeister, Rahul Satija (2019)

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

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Dimensionality reduction for visualizing single-cell data using UMAP

Etienne Becht, Leland McInnes, John Healy … (2018)

Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

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Author and article information

Journal

Journal ID (nlm-journal-id): 0404511

Journal ID (pubmed-jr-id): 7473

Journal ID (nlm-ta): Science

Journal ID (iso-abbrev): Science

Title: Science (New York, N.Y.)

ISSN (Print): 0036-8075

ISSN (Electronic): 1095-9203

Publication date Nihms-submitted: 5 July 2022

Publication date (Print): July 2022

Publication date (Electronic): 30 June 2022

Publication date PMC-release: 08 July 2022

Volume: 377

Issue: 6601

Pages: 56-62

Affiliations

[1 ]Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA 02138, USA.

[2 ]Allen Institute for Brain Science, Seattle, WA 98109, USA.

Author notes

[*]

These authors contributed equally to this work

Author contributions: E.L. and X.Z. conceived the study. R.F., C.X., and X.Z. designed experiments with input from J.C., M.Z., J.H., B.L., J.M. and E.L.. M.Z. and J.H. performed human tissue MERFISH protocol testing. C.X. and J.M. designed the MERFISH gene panel. J.C. performed human tissue processing and quality testing. R.F., C.X., Z.H., and A.H. performed MERFISH experiments. R.F. and C.X. performed data analysis. R.F., C.X., J.C., B.L., J.M., E.L., and X.Z. evaluated experimental results. R.F. and X.Z. wrote the paper with input from C.X., J.C., M.Z., J.H., Z.H., A.H., B.L., J.M. and E.L.. X.Z. oversaw the project.

[† ]Corresponding author. zhuang@ 123456chemistry.harvard.edu (X.Z.)

Article

Manuscript ID: NIHMS1820958

DOI: 10.1126/science.abm1741

PMC ID: 9262715

PubMed ID: 35771910

SO-VID: 77411063-aa4d-4b68-9432-f2c6471dc9c0

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Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH

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Most cited references 60

Comprehensive Integration of Single-Cell Data

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Dimensionality reduction for visualizing single-cell data using UMAP

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