# Defining human hematopoietic cells via transcriptomes and methods to isolate them

> **NIH NIH R21** · CINCINNATI CHILDRENS HOSP MED CTR · 2020 · $198,750

## Abstract

PROJECT SUMMARY
Currently, the methods utilized to describe cell states include defining populations via flow cytometry,
examining developmental potentials using in vitro colony-forming-unit assays and in vivo genetic marking, and
genomics analyses such as single-cell RNA-Seq (scRNA-Seq) and scATAC-Seq. While these complementary
analyses define a multitude of cell states, there is a lack of coherence between the resulting observations
partly due to lack of uniform approaches between labs. The end result has been considerable confusion or
controversy rather than a consolidation of understanding. To address this fundamental problem in the field we
have assembled an interdisciplinary research team which encompasses expertise in the application of single-
cell technologies, hematopoiesis, computational genomics and systems biology to develop and promote a
unifying framework for the analysis of genomic states. Specifically, based principally on their genomic states,
we will define prevalent and rare hematopoietic intermediates, and the optimal markers and flow gates
necessary to isolate them. Deliverables will include a consolidated understanding of the hematopoietic
hierarchy based on cutting edge technologies.

## Key facts

- **NIH application ID:** 9871580
- **Project number:** 1R21HL150678-01
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** H. LEIGHTON GRIMES
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,750
- **Award type:** 1
- **Project period:** 2020-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9871580

## Citation

> US National Institutes of Health, RePORTER application 9871580, Defining human hematopoietic cells via transcriptomes and methods to isolate them (1R21HL150678-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9871580. Licensed CC0.

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