# Developing a Novel Algorithm to Infer Cellular Trajectories from Single-Cell RNA Sequencing Data in Hematopoiesis

> **NIH NIH F30** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $53,974

## Abstract

Project Summary/Abstract:
Single-cell RNA sequencing (scRNA-seq) has unveiled significant variability among hematopoietic stem and
progenitor cells (HSPCs) and developing erythroid cells. Despite these advancements, current computational
tools fall short in accurately identifying transient and mixed-lineage states crucial for erythroid fate specification.
This gap in understanding and classification poses a significant challenge, as these rare states play a pivotal
role in hematopoiesis, characterized by a complex continuum of cellular states rather than a simple branching
hierarchy. Addressing this challenge, our research introduces two innovative algorithms: the first aims to classify
distinct cell identities and capture elusive transient states without prior biological assumptions; the second seeks
to elucidate the optimal trajectory of transcriptional states during HSPC to erythrocyte differentiation. Our primary
goal is to identify the mixed-lineage intermediates and the gene regulatory networks guiding erythroid cell fate.
This includes investigating Megakaryocyte-Erythroid Progenitors and potential upstream multipotential transition
cells, which are currently not well-characterized. In our first aim we will develop a clustering algorithm based on
algebraic topology. This algorithm will analyze scRNA-seq data to identify novel cell states and infer their
developmental relationships. Our approach surpasses existing methods by eliminating the need for selecting
hyperparameters and by effectively capturing intermediate states. We will validate and benchmark our algorithm
using established single-cell datasets of early myeloid progenitor differentiation in adult mice. In our second aim,
we will create a probabilistic cell pathway trajectory model. This innovative approach incorporates both biological
stochasticity and technical measurement noise, enabling a more accurate prediction of activated and inhibited
gene programs in erythropoiesis. We envision that our approach will help filter out time-dependent gene
regulatory programs, offering a new understanding of HSPC trajectories to erythrocytes. We will validate our
predicted genes and their contribution to erythrocyte biogenesis experimentally. Upon completion, this study is
expected to uncover novel cell subpopulations and transitional states in adult mouse erythropoiesis. We aim to
decode the gene regulatory programs behind these transitions and understand the sequence and interplay of
transcription factors and gene programs in erythropoiesis. This comprehensive understanding will enable us to
propose new cell engineering protocols for efficient and controlled differentiation, potentially transforming
approaches in hematopoietic research and therapy. A comprehensive training plan will develop the principal
investigator’s skills as a physician-scientist under the guidance of the sponsor (Jie Liang, PhD, University of
Illinois at Chicago) and co-sponsor (Konstantinos Chronis, PhD, University ...

## Key facts

- **NIH application ID:** 10998283
- **Project number:** 1F30HL176078-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Ali Mohamad Farhat
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $53,974
- **Award type:** 1
- **Project period:** 2024-09-30 → 2028-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998283

## Citation

> US National Institutes of Health, RePORTER application 10998283, Developing a Novel Algorithm to Infer Cellular Trajectories from Single-Cell RNA Sequencing Data in Hematopoiesis (1F30HL176078-01). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10998283. Licensed CC0.

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