Bold Findings About Blood Cell Formation Stir the Scientific Pot

Published Aug. 31, 2016
Nature

H. Leighton Grimes, PhD, describes his team’s recent insights into how blood cells determine their developmental fate as a genetic tug of war.

As early-stage progenitor cells develop into the various components of our blood, they do so amid a swirl of genetic turbulence, or dynamic instability, which can be observed by turning on alternate lineage genes in individual cells.

The cells briefly exist in these multi-lineage states, and sometimes in a less common bi-stable state, before moving on to become neutrophils, monocytes, etc. The ultimate fate of the cell is determined through a competition between opposing gene regulatory networks.

By finding gene networks that appear to regulate normal blood cell formation, the team also suggests that miscues in this process may explain a great deal about a variety of blood and immune system disorders. Grimes and colleagues detailed their theory in a paper published in Nature in August 2016—and set the scientific community abuzz.

“While the data were initially controversial, the paper has been cited 31 times,” Grimes says. “The study is making a contribution and people still discuss it at scientific meetings because others are finding similar results.”

Deep vs. Wide Debate

The paper was a collaboration between Grimes,  Harinder Singh, PhD, Director of Immunobiology, and Nathan Salomonis, PhD, Biomedical Informatics. The study infers that within these genetic tugs of war, other as yet undiscovered multi-lineage intermediates likely exist.

The team used emerging single-cell RNA sequencing technology, which can identify different genes and regulatory networks within individual cells, as a critical tool for reaching their conclusions. However, scientists sometimes disagree over how best to use the tool.

Some researchers favor sequencing and analyzing thousands of cells without diving as deeply into the different genes that are switching on and off. However, the Cincinnati Children’s team opted to sequence only about 500 cells. They probed much more deeply into the gene expression patterns and regulatory networks involved in the blood cell tug of war. “This is a major conversation around sequencing thousands of cells at low depth versus fewer cells at deeper depth,” Singh says.

Tapping a Bioinformatics Pipeline

However, the Cincinnati Children’s project involved more than single-cell RNA sequencing to draw its conclusions.

Among the other experiments involved: a new bioinformatics pipeline developed by Salomonis called Iterative Clustering and Guide-Gene Selection (ICGS). This automated platform helps scientists process and analyze all of the single-cell RNA sequencing data to identify the transitioning or shifting genomic states of cells.

“Our analysis captured prevalent mixed-lineage intermediates that manifested coincident expression of hematopoietic stem cell/progenitor (HSCP) and myeloid progenitor genes,” the co-authors say. “It also revealed rare metastable intermediates that had collapsed the HSCP program and expressed low levels of the myeloid determinants, Irf8 and Gfi1.”

ICGS resolved nine hierarchically-ordered cellular states, and further analysis detailed their cellular identities: HSCP-1 (Hema-topoietic Stem Cell Progenitor), HSCP-2, Meg (Megakaryocytic), Eryth (Erythrocytic), Multi-Lin* (Multi-Lineage Primed), MDP (Monocyte-Dendritic cell precursor), Mono (Monocytic), Gran (Granulocytic) and Myelocyte (myelocytes and metamyelocytes).

“Strikingly, the unbiased ICGS analysis inferred a developmental order in agreement with the experimentally determined hematopoietic sequence,” the authors wrote.

Now the ICGS platform is attracting interest from other research centers.

"A number of other researchers are using bioinformatics tools that we developed to test new hypotheses,” says Salomonis. “We also are in touch with other research groups who are using data we generated in the Nature paper to test different hypotheses for the exact cell populations we describe.”

These figures describe how a research team “trapped” a rare myeloid transition state during the blood cell development process by removing counteracting determinants. A: Shows the outcome of ICGS-based scRNA-Seq analysis of GG1 and IG2 cells. Known hematopoietic regulators and markers are indicated to the right). B: HOPACH clustering of WT, IG2 and Irf8−/−Gfi1−/− GMPs based on ICGS-delineated genes, with indicated myeloid cellular states shown to the right. C: Monocyte or granulocyte gene enrichment analysis compared the median expression value of a gene within cells of designated group to its median value in all cells. Average fold change (log2 ± SEM) was determined for monocytic or granulocytic genes.

Click image to enlarge.

This model depicts a hierarchical set of hematopoietic intermediates culminating in the specification of monocytic and granulocytic lineages. Cells are ordered on a Waddington landscape with their characteristic gene expression modules (color bars) and states. The prevalent (Multi-Lin* ) and rare (bistable) mixed-lineage myeloid transition states are proposed to manifest dynamic instability because of counteracting regulatory determinants. Although erythroid and megakaryocytic progenitor cells were found within CMP cell populations, it remains to be determined whether a distinct set of Multi-Lin* intermediates give rise to these progenitors via a rare bi-stable state.

Click image to enlarge.

Citation

Olsson A, Venkatasubramanian M, Chaudhri VK, Aronow BJ, Salomonis N, Singh H, Grimes HL. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature. 2016 537(7622):698-702.