Dataset Information

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types.

ABSTRACT: We present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica and describe how its component cell types respond to perturbation. Using multiplexed single-cell RNA sequencing, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and regeneration in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole-animal, multiplexed single-cell genomics that is readily adaptable to other traditional or nontraditional model organisms.

SUBMITTER: Chari T

PROVIDER: S-EPMC8626072 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across <i>Clytia</i> medusa cell types.

Chari Tara T Weissbourd Brandon B Gehring Jase J Ferraioli Anna A Leclère Lucas L Herl Makenna M Gao Fan F Chevalier Sandra S Copley Richard R RR Houliston Evelyn E Anderson David J DJ Pachter Lior L

Science advances 20211126 48

We present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa <i>Clytia hemisphaerica</i> and describe how its component cell types respond to perturbation. Using multiplexed single-cell RNA sequencing, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation ...[more]

PMID: 34826233

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Project description:Maintenance of systemic homeostasis by kidney requires the coordinated response of diverse cell types. The use of single-cell RNA sequencing (scRNAseq) for patient tissue samples remains fraught with difficulties with cell isolation, purity, and experimental bias. The ability to characterize immune and parenchymal cells during transplant rejection will be invaluable in defining transplant pathology where tissue availability is restricted to needle biopsy fragments. Herein, we present feasibility data for multiplexing approach for droplet scRNAseq (Mux-Seq). Mux-Seq has the potential to minimize experimental batch bias and variation even with very small sample input. In this first proof-of-concept study for this approach, explant tissues from six normal and two transplant recipients after multiple early post-transplant rejection episodes leading to nephrectomy due to aggressive antibody mediated rejection, were pooled for Mux-Seq. A computational tool, Demuxlet was applied for demultiplexing the individual cells from the pooled experiment. Each sample was also applied individually in a single microfluidic run (singleplex) to correlate results with the pooled data from the same sample. Our applied protocol demonstrated that data from Mux-Seq correlated highly with singleplex (Pearson coefficient 0.982) sequencing results, with the ability to identify many known and novel kidney cell types including different infiltrating immune cells. Trajectory analysis of proximal tubule and endothelial cells demonstrated separation between healthy and injured kidney from transplant explant suggesting evolving stages of cell- specific differentiation in alloimmune injury. This study provides the technical groundwork for understanding the pathogenesis of alloimmune injury and host tissue response in transplant rejection and normal human kidney and provides a protocol for optimized processing precious and low input human kidney biopsy tissue for larger scale studies.

Dataset Information

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types.

Publications

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across <i>Clytia</i> medusa cell types.

Similar Datasets

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