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Chen, Fei

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Chen

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Fei

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Chen, Fei
Author's Publications: search.filters.isAuthorOfPublication.bce07700-594e-477b-bd6e-3725fece6b94

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    Robust decomposition of cell type mixtures in spatial transcriptomics
    (SpringerNature, 2020-05-08) Cable, Dylan; Murray, Evan; Zou, Luli; Goeva, Aleksandrina; Macosko, Evan; Chen, Fei; Irizarry, Rafael
    Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD’s recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.
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    Compressed sensing for highly efficient imaging transcriptomics
    (Springer Science and Business Media LLC, 2021-04-15) Cleary, Brian; Simonton, Brooke; Bezney, Jon; Murray, Evan; Alam, Shahul; Sinha, Anubhav; Habibi, Ehsan; Marshall, Jamie; Lander, Eric S.; Chen, Fei; Regev, Aviv
    Tissue and organ function rely on the organization of cells and molecules in specific spatial structures. In order to understand these structures and how they relate to tissue function in health and disease, we would ideally be able to rapidly profile gene expression over large tissue volumes. To this end, in recent years multiple molecular assays have been developed that can image from a dozen to ~100 individual proteins or RNAs in a sample at single-cell resolution, with barcodes to allow multiplexing across genes. These approaches have serious limitations with respect to (i) the number of genes that can be studied; and (ii) imaging time, due to the need for high-resolution to resolve individual signals. Here, we show that both challenges can be overcome by introducing an approach that leverages the biological fact that gene expression is often structured across both cells and tissue organization. We develop Composite In Situ Imaging (CISI), that combines this biological insight with algorithmic advances in compressed sensing to achieve greater efficiency. We demonstrate that CISI accurately recovers the spatial abundance of each of 37 individual genes from 11 composite measurements in 12 bisected mouse brain coronal sections covering 180mm and 476,276 cells without the need for spot-level resolution. CISI achieves the current scale of multiplexing with two orders of magnitude greater efficiency, and can be leveraged in combination with existing methods to multiplex far beyond current scales.
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    Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
    (Springer Science and Business Media LLC, 2020-12-07) Stickels, Robert; Murray, Evan; Kumar, Pawan; Li, Jilong; Marshall, Jamie; Di Bella, Daniela; Arlotta, Paola; Macosko, Evan; Chen, Fei
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    Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
    (Springer Science and Business Media LLC, 2023-12-13) Russell, Andrew J. C.; Weir, Jackson A.; Nadaf, Naeem M.; Shabet, Matthew; Kumar, Vipin; Kambhampati, Sandeep; Raichur, Ruth; Marrero, Giovanni J.; Liu, Sophia; Balderrama, Karol S.; Vanderburg, Charles R.; Shanmugam, Vignesh; Tian, Luyi; Iorgulescu, J. Bryan; Yoon, Charles H.; Wu, Catherine J.; Macosko, Evan Z.; Chen, Fei
    Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1–6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor–ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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    Spatial multiomic landscape of the human placenta at molecular resolution
    (Springer Science and Business Media LLC, 2024-11-20) Ounadjela, Johain R.; Zhang, Ke; Kobayashi-Kirschvink, Koseki J.; Jin, Kang; J. C. Russell, Andrew; Lackner, Andreas I.; Callahan, Claire; Viggiani, Francesca; Dey, Kushal K.; Jagadeesh, Karthik; Maxian, Theresa; Prandstetter, Anna-Maria; Nadaf, Naeem; Gong, Qiyu; Raichur, Ruth; Zvezdov, Morgan L.; Hui, Mingyang; Simpson, Mattew; Liu, Xinwen; Min, Wei; Knöfler, Martin; Chen, Fei; Haider, Sandra; Shu, Jian
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    The molecular cytoarchitecture of the adult mouse brain
    (Springer Science and Business Media LLC, 2023-12-13) Langlieb, Jonah; Sachdev, Nina S.; Balderrama, Karol S.; Nadaf, Naeem M.; Raj, Mukund; Murray, Evan; Webber, James T.; Vanderburg, Charles; Gazestani, Vahid; Tward, Daniel; Mezias, Chris; Li, Xu; Flowers, Katelyn; Cable, Dylan M.; Norton, Tabitha; Mitra, Partha; Chen, Fei; Macosko, Evan Z.
    The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq1,2—a recently developed spatial transcriptomics method with near-cellular resolution—across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource (www.BrainCellData.org), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases.
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    Molecular Logic of Cellular Diversification in the Mouse Cerebral Cortex
    (Springer Science and Business Media LLC, 2021-06-23) Di Bella, Daniela; Habibi, Ehsan; Stickels, Robert; Scalia, Gabriele; Brown, Juliana; Yadollahpour, Payman; Yang, Sung Min; Abbate, Catherine; Biancalani, Tommaso; Macosko, Evan; Chen, Fei; Regev, Aviv; Arlotta, Paola
    The mammalian cerebral cortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function. However, the molecular logic that governs the establishment and organization of cortical cell types remains elusive, largely due to the large number of cell classes undergoing dynamic cell-state transitions over extended developmental timelines. Here, we have generated a comprehensive single-cell RNA-seq and single-cell ATAC-seq atlas of the developing mouse neocortex, sampled every day throughout embryonic corticogenesis and at early postnatal ages, complemented with a spatial transcriptomics time-course. We computationally reconstruct developmental trajectories across the diversity of cortical cell classes, and infer their spatial organization and the gene regulatory programs that accompany their lineage bifurcation decisions and differentiation trajectories. Finally, we demonstrate how this developmental map pinpoints the origin of lineage-specific developmental abnormalities linked to aberrant corticogenesis in mutant animals. The data provides a global picture of the regulatory mechanisms governing cellular diversification in the neocortex.