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Single cell RNA-seq and ATAC-seq data integration for macrophage gene networks
Summary Data Summary
Applicant Dai, Yang
E-Mail Address yangdai@uic.edu
Project Title Single cell RNA-seq and ATAC-seq data integration for macrophage gene networks
CBU ID 21AU4169
External SubContract ID 32307-91
Diabetic Complication Wound Healing
Funding Program Group Pilot & Feasibility [PF2021]
Abstract Chronic wounds represent an escalating health problem around the world,
especially in diabetic patients.. Although multiple factors contribute to the
development and impaired healing of chronic wounds, a common characteristic of
these poorly healing wounds is a persistent inflammatory response, including the
accumulation of pro-inflammatory macrophages. Macrophages are capable of
performing a diverse array of tasks during wound healing, ranging from
destructive killing functions to pro-healing and homeostatic duties. Recent
studies have demonstrated that macrophages adopt a spectrum of phenotypes,
especially in vivo, which are difficult to fit into the (pro-inflammatory)/M2
(pro-healing) scheme. In addition to the limited and biased characterization of
macrophage phenotypes during wound healing, the regulation of macrophage
phenotypes remains poorly understood. We have generated single cell RNAseq
(scRNAseq) datasets on macrophages isolated from skin wounds to assess
heterogeneity of mRNA profiles and are starting to generate parallel single cell
ATACseq (scATACseq) datasets to identify locations of accessible chromatin and
thus the regulatory landscape of genes. Although these data will provide
valuable information on macrophage heterogeneity during wound healing,
computational methods for integrating scRNAseq and scATACseq data are currently
limited. A critical first step in analysis of scATACseq data is to enrich
signals by grouping cells into clusters (pseudo-bulk) of a similar epigenetic
profile for downstream analysis such as chromatin accessibility and
transcription factor and target identification. Currently, this step of signal
enrichment is carried out using data-driven procedures on scATACseq data alone
in most of the tools. We hypothesize that generating cell clusters by modeling
transcription factor activities from scRNAseq data will be more biologically
relevant and could be a better alternative for signal enrichment in scATACseq
data analysis to deliver better downstream results. In Aim A, we will use the
BITFAM generated clusters as pseudo-bulks to enrich the scATACseq signals. The
downstream analysis of scATACseq data will generate context-specific
transcription factor gene targets, which in turn will inform BITFAM to generate
better clustering of cells and facilitate the new iteration of scATACseq signal
enrichment. In Aim B, we will repeat the procedure in Aim A until it converges
to a cell clustering result where each cluster is formed from cells with shared
transcription factor activities and gene targets supported by the two omics
data. Impact: Developing computational methods for integrating single cell
RNAseq and ATACseq data will enable a better understanding of the transcription
factor networks critical for regulating macrophage heterogeneity over the course
of normal and impaired healing, and could be extended to other cell types,
tissues and disease states.
Application PDF Application Research Plan
Status Contract Executed
Key Personnel PI: Yang Dai, PhD
Co-PI: Timothy Koh, PhD
RA:Mehrdad Zandigohar
Salary Total Costs 35666
Supply Total Costs 0
Equipment Total Costs 0
Travel/Other Total Costs 10658
Direct Costs 46324
Indirect Costs Proposed 22561
Total Costs Proposed 68885
Total Costs Approved 61997
Start Date 7/1/2021
End Date 6/30/2023
IFO Name McCormack, Karen
IFO E-Mail Address awards@uic.edu
IACUC/IRB No. 99999
IACUC/IRB Institution University of Illinois at Chicago
Entity ID No.
Report Request Date 6/30/2023
T1D NO
TypeCount
Invoices 14
Progress Reports 2
Data Submission


Invoices
UrlCBU IDExternal IDInstitutionDateDirectIndirectInvoiceBalancePDF
  View  21AU416932307-91University of Illinois at Chicago9/12/2022$1,826.39$778.27$2,604.66$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago8/11/2022$2,681.28$1,448.21$4,129.49$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago7/18/2022$10,768.12$6,292.19$17,060.31$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago7/17/2023$2,094.95$28.31$2,123.26$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago6/9/2022$3,354.30$1,432.97$4,787.27$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago5/10/2022$4,083.13$1,744.33$5,827.46$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago4/11/2022$3,354.30$1,432.97$4,787.27$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago3/9/2022$3,354.30$1,432.97$4,787.27$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago2/9/2023$1,976.72$1,018.67$2,995.39$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago2/9/2022$1,524.85$651.42$2,176.27$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago12/9/2022$1,826.41$778.28$2,604.69$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago11/10/2022$1,826.41$778.28$2,604.69$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago10/11/2022$1,826.41$778.28$2,604.69$2,904.28View PDF
  View  21AU416932307-91University of Illinois at Chicago ---$2,904.28View PDF
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