MassIVE MSV000081369

Partial Public

Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types

Description

Mead BE, Ordovas-Montanes J, Braun AP, Levy LE, Bhargava P, Szucs MJ, Ammendolia DA, MacMullan MA, Zheng Y, Yin X, Hughes TK, Wadsworth MH, Ahmad R, Rakoff-Nahoum S, Carr SA, Langer R, Collins JJ, Shalek AK, Karp JM. BMC Biology 2018. Background: Single-cell genomic methods now provide unprecedented resolution for characterizing the component cell types and states of tissues, such as the epithelial subsets of the gastrointestinal tract. Nevertheless, functional studies of these subsets at scale require faithful in vitro models of identified in vivo biology. While intestinal organoids have been invaluable in providing mechanistic insights in vitro, the extent to which organoid-derived cell types recapitulate their in vivo counterparts remains formally untested, with no systematic approach for improving model fidelity. Results: Here, we present a generally applicable framework that utilizes massively-parallel single-cell RNA-seq to compare cell types and states found in vivo to those of in vitro models, such as organoids. Furthermore, we leverage identified discrepancies to improve model fidelity. Using the Paneth cell (PC), which supports the stem cell niche and produces the largest diversity of antimicrobials in the small intestine, as an exemplar, we uncover fundamental gene expression differences in lineage-defining genes between in vivo PCs and those of the current in vitro organoid model. With this information, we nominate a molecular intervention to rationally improve the physiological fidelity of our in vitro PCs. We then perform transcriptomic, cytometric, morphologic, and proteomic characterization, and demonstrate functional (antimicrobial activity, niche support) improvements in Paneth cell physiology. Conclusions: Our systematic approach provides a simple workflow for identifying the limitations of in vitro models and enhancing their physiological fidelity. Using adult stem cell-derived PCs within intestinal organoids as a model system, we successfully benchmark organoid representation relative to in vivo of a specialized cell type and use this comparison to generate a functionally improved in vitro PC population. We predict that the generation of rationally-improved cellular models will facilitate mechanistic exploration of specific disease-associated genes in their respective cell types. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: TMT ; Paneth

Contact

Principal Investigators:
(in alphabetical order)
Steven A. Carr, Broad Institute of MIT and Harvard, United States
Submitting User: clauser
Number of Files:
Total Size:
Spectra:
Subscribers:
 
Owner Reanalyses
Experimental Design
    Conditions:
    Biological Replicates:
    Technical Replicates:
 
Identification Results
    Proteins (Human, Remapped):
    Proteins (Reported):
    Peptides:
    Variant Peptides:
    PSMs:
 
Quantification Results
    Differential Proteins:
    Quantified Proteins:
 
Browse Dataset Files
 
FTP Download Link (click to copy):

- Dataset Reanalyses


+ Dataset History


Click here to queue conversion of this dataset's submitted spectrum files to open formats (e.g. mzML). This process may take some time.

When complete, the converted files will be available in the "ccms_peak" subdirectory of the dataset's FTP space (accessible via the "FTP Download" link to the right).
Number of distinct conditions across all analyses (original submission and reanalyses) associated with this dataset.

Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

"N/A" means no results of this type were submitted.
Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

"N/A" means no results of this type were submitted.
Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.