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| Status |
Public on May 19, 2023 |
| Title |
Mapping disease-associated regulatory circuits by cell type from single-cell multiomics data (S. aureus scRNA-seq) |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Resolving chromatin remodeling-linked gene expression changes is important for understanding disease states. We describe MAGICAL (Multiome Accessible Gene Integration Calling And Looping), a hierarchical Bayesian approach that leverages paired scRNA-seq and scATAC-seq data from different conditions to map disease-associated transcription factors, regulatory sites and genes as regulatory circuits. By introducing hidden Bayesian variables to allow modeling noise and signal variation across cells and conditions in both transcriptome and chromatin data, in systemic evaluations MAGICAL achieved high accuracy on circuit prediction at cell-type resolution. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single cell data that we generated from infected subjects and healthy uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be sepsis activated. We addressed the challenging problem of distinguishing methicillin-resistant- (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA) infections, where differential expression analysis failed to show predictive value. MAGICAL, however, identified epigenetic circuit biomarkers that distinguished MRSA from MSSA.
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| Overall design |
scRNA sequencing of PBMCs from 10 samples with MRSAs, 11 samples with MSSAs and 23 healthy controls
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| Web link |
https://doi.org/10.1038/s43588-023-00476-5
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| Contributor(s) |
Chen X, Zaslavsky E |
| Citation(s) |
37974651 |
| Submission date |
Dec 06, 2022 |
| Last update date |
Dec 07, 2023 |
| Contact name |
Elena Zaslavsky |
| E-mail(s) |
elena.zaslavsky@mssm.edu
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| Organization name |
Icahn School of Medicine at Mount Sinai
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| Street address |
1 Gustave L. Levy Pl
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| City |
New York |
| State/province |
NY |
| ZIP/Postal code |
10029 |
| Country |
USA |
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| Platforms (1) |
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| Samples (44)
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| GSM6793464 |
AS11-07049, Control, scRNAseq |
| GSM6793465 |
AS11-07881, Control, scRNAseq |
| GSM6793466 |
AS11-12162, Control, scRNAseq |
| GSM6793467 |
AS11-18755, Control, scRNAseq |
| GSM6793468 |
AS13-13951, Control, scRNAseq |
| GSM6793469 |
AS13-08590, Control, scRNAseq |
| GSM6793470 |
AS14-00902, Control, scRNAseq |
| GSM6793471 |
AS14-03700, Control, scRNAseq |
| GSM6793472 |
AS17-00144, Control, scRNAseq |
| GSM6793473 |
AS17-02129, Control, scRNAseq |
| GSM6793474 |
AS18-00669, Control, scRNAseq |
| GSM6793475 |
BMI0037, Control, scRNAseq |
| GSM6793476 |
BMI0040, Control, scRNAseq |
| GSM6793477 |
BMI0093, Control, scRNAseq |
| GSM6793478 |
BMI0094, Control, scRNAseq |
| GSM6793479 |
BMI0095, Control, scRNAseq |
| GSM6793480 |
BMI0099, Control, scRNAseq |
| GSM6793481 |
BMI0101, Control, scRNAseq |
| GSM6793482 |
BMI0102, Control, scRNAseq |
| GSM6793483 |
BWJ0023, Control, scRNAseq |
| GSM6793484 |
DU19-01S0003453, MSSA, scRNAseq |
| GSM6793485 |
DU19-01S0003462, MSSA, scRNAseq |
| GSM6793486 |
DU19-01S0003464, MSSA, scRNAseq |
| GSM6793487 |
DU19-01S0003466, MSSA, scRNAseq |
| GSM6793488 |
DU19-01S0003482, MSSA, scRNAseq |
| GSM6793489 |
DU19-01S0003492, MSSA, scRNAseq |
| GSM6793490 |
DU19-01S0003507, MSSA, scRNAseq |
| GSM6793491 |
DU19-01S0003509, MSSA, scRNAseq |
| GSM6793492 |
DU19-01S0003515, MSSA, scRNAseq |
| GSM6793493 |
DU19-01S0003527, MSSA, scRNAseq |
| GSM6793494 |
DU19-01S0003542, MRSA, scRNAseq |
| GSM6793495 |
DU19-01S0003549, MRSA, scRNAseq |
| GSM6793496 |
DU19-01S0003978, MRSA, scRNAseq |
| GSM6793497 |
DU19-01S0003987, MRSA, scRNAseq |
| GSM6793498 |
DU19-01S0003989, MRSA, scRNAseq |
| GSM6793499 |
DU19-01S0003992, MRSA, scRNAseq |
| GSM6793500 |
DU19-01S0003994, MRSA, scRNAseq |
| GSM6793501 |
DU19-01S0004011, MRSA, scRNAseq |
| GSM6793502 |
DU19-01S0004013, MRSA, scRNAseq |
| GSM6793503 |
DU19-01S0004015, MRSA, scRNAseq |
| GSM6793504 |
DU19-01S0004017, MSSA, scRNAseq |
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| This SubSeries is part of SuperSeries: |
| GSE220190 |
Mapping disease-associated regulatory circuits by cell type from single-cell multiomics data |
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| Relations |
| BioProject |
PRJNA909218 |
| SRA |
SRP411630 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE220189_MRSA-MSSA-CTRL-all-combine-20210908.RData.gz |
14.7 Gb |
(ftp)(http) |
RDATA |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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