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

University Of Illinois At Chicago, Chicago IL

Investigators

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 of Illinois at Chicago).