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      Molecular and spatial profiling of the paraventricular nucleus of the thalamus

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          Abstract

          The paraventricular nucleus of the thalamus (PVT) is known to regulate various cognitive and behavioral processes. However, while functional diversity among PVT circuits has often been linked to cellular differences, the molecular identity and spatial distribution of PVT cell types remain unclear. To address this gap, here we used single nucleus RNA sequencing (snRNA-seq) and identified five molecularly distinct PVT neuronal subtypes in the mouse brain. Additionally, multiplex fluorescent in situ hybridization of top marker genes revealed that PVT subtypes are organized by a combination of previously unidentified molecular gradients. Lastly, comparing our dataset with a recently published single-cell sequencing atlas of the thalamus yielded novel insight into the PVT’s connectivity with the cortex, including unexpected innervation of auditory and visual areas. This comparison also revealed that our data contains a largely non-overlapping transcriptomic map of multiple midline thalamic nuclei. Collectively, our findings uncover previously unknown features of the molecular diversity and anatomical organization of the PVT and provide a valuable resource for future investigations.

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          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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            Fast, sensitive, and accurate integration of single cell data with Harmony

            The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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              Massively parallel digital transcriptional profiling of single cells

              Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                03 March 2023
                2023
                : 12
                : e81818
                Affiliations
                [1 ] National Institute of Mental Health ( https://ror.org/04t0s7x83) Bethesda United States
                [2 ] Department of Neuroscience, Brown University ( https://ror.org/05gq02987) Providence United States
                [3 ] National Institute on Alcohol Abuse and Alcoholism ( https://ror.org/02jzrsm59) Rockville United States
                [4 ] National Institute of Child Health and Human Development ( https://ror.org/04byxyr05) Bethesda United States
                Duke University School of Medicine ( https://ror.org/00py81415) United States
                The University of Texas at Austin ( https://ror.org/00hj54h04) United States
                Duke University School of Medicine ( https://ror.org/00py81415) United States
                Duke University School of Medicine ( https://ror.org/00py81415) United States
                Medical University of South Carolina ( https://ror.org/012jban78) United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-0335-0730
                https://orcid.org/0000-0002-5368-1802
                Article
                81818
                10.7554/eLife.81818
                10014079
                36867023
                26575018-19bc-4c80-89c4-f123363af4b1

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 12 July 2022
                : 02 March 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: 1ZIAMH002950
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: ZIANS003153
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Molecular and spatial profiling methods idenified five neuronal subtypes that are distributed along the anteroposterior axis of the paraventricular thalamus, a structure increasingly implicated in the control of emotional and motivated behaviors.

                Life sciences
                paraventricular thalamus,single-nuclei sequencing,cell types,mouse
                Life sciences
                paraventricular thalamus, single-nuclei sequencing, cell types, mouse

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