https://orcid.org/0000-0002-5055-4859
Figures
Abstract
Erythroid cells, serving as progenitors and precursors to erythrocytes responsible for oxygen transport, were shown to exhibit an immunosuppressive and immunoregulatory phenotype. Previous investigations from our research group have revealed an antimicrobial gene expression profile within murine bone marrow erythroid cells which suggested a role for erythroid cells in innate immunity. In the present study, we focused on elucidating the characteristics of human bone marrow erythroid cells through comprehensive analyses, including NanoString gene expression profiling utilizing the Immune Response V2 panel, a BioPlex examination of chemokine and TGF-beta family proteins secretion, and analysis of publicly available single-cell RNA-seq data. Our findings demonstrate that an erythroid cell subpopulation manifests a myeloid-like gene expression signature comprised of antibacterial immunity and neutrophil chemotaxis genes which suggests an involvement of human erythroid cells in the innate immunity. Furthermore, we found that human erythroid cells secreted CCL22, CCL24, CXCL5, CXCL8, and MIF chemokines. The ability of human erythroid cells to express these chemokines might facilitate the restriction of immune cells in the bone marrow under normal conditions or contribute to the ability of erythroid cells to induce local immunosuppression by recruiting immune cells in their immediate vicinity in case of extramedullary hematopoiesis.
Citation: Perik-Zavodskii R, Perik-Zavodskaia O, Shevchenko J, Volynets M, Alrhmoun S, Nazarov K, et al. (2024) A subpopulation of human bone marrow erythroid cells displays a myeloid gene expression signature similar to that of classic monocytes. PLoS ONE 19(7): e0305816. https://doi.org/10.1371/journal.pone.0305816
Editor: Francesco Bertolini, European Institute of Oncology, ITALY
Received: April 9, 2024; Accepted: June 5, 2024; Published: July 22, 2024
Copyright: © 2024 Perik-Zavodskii et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The NanoString gene expression data were deposited to the Gene Expression Omnibus (GEO) with the accession code GSE231655. Erythroid cell scRNA-seq data is available at the Gene Expression Omnibus (GEO) with the accession code GSE199230. Myeloid cell scRNA-seq data is available at the Gene Expression Omnibus (GEO) with the accession code GSE261733. CD235-positive Erythroid cell analysis data and Myeloid-like Erythroid cell comparison with other myeloid cell data via Seurat is available at https://github.com/Romanzavodskii/Myeloid-like-Erythroid-Cells.
Funding: This research was funded by Ministry of Higher Education and Science, State Assignment No. 0415-2024-0012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Erythroid cells, formerly deemed as neglected cells [1], have recently garnered increased attention from researchers owing to their discernible immunoregulatory functionalities. Extensive investigations involving both human and murine erythroid cells, facilitated by techniques such as flow cytometry, bulk RNA-seq, and qPCR, have elucidated their capacity to generate and express a diverse array of immunosuppressive molecules. Noteworthy among these are interleukin-10 (IL-10) [2, 3] and transforming growth factor-beta 1 (TGF-beta1) [4, 5] cytokines, as well as the production of reactive oxygen species (ROS) [6, 7]. Additionally, erythroid cells have been found to express programmed cell death ligand-1 (PD-L1) [5], PD-L2 [6], and V-domain Ig suppressor of T cell activation (VISTA) [8] inhibitory checkpoint molecules, along with the enzymatic activities of Arginase-1 and Arginase-2 [5, 9–11].
While some researchers have posited the immunoregulatory properties of erythroid cells based on their synthesis of a diverse repertoire of multidirectional cytokines with both pro- and anti-inflammatory attributes [12–17], it is noteworthy that existing data is contradictory in nature. Variability arises particularly in delineating the precise mechanisms employed by erythroid cells to exert their immunoregulatory influence.
In our preceding investigation [4], we identified an antibacterial gene expression signature in murine bone marrow erythroid cells, wherein the genes S100a8, S100a9, and Camp exhibited the highest overall expression levels. This myeloid cell-like gene expression signature in murine erythroid cells raised the question of whether such genes are also expressed in human erythroid cells.
In a recent study of human erythroid cells [18], a novel population of cells termed erythroid-derived myeloid cells (EDMCs) was characterized as "transcriptionally indistinguishable from their myeloid-derived counterparts." These cells emerge in cancer patients from CD45-positive erythroid cells, induced by tumor-derived granulocyte-macrophage colony-stimulating factor (GM-CSF). This phenomenon adds another dimension to the versatile nature of erythroid cells, demonstrating myeloid properties, particularly those associated with immunosuppression, albeit in a non-steady state.
Presently, the transferrin receptor CD71 stands as the predominant marker employed for the study of erythroid cells. This marker has been described as a marker for the enrichment of erythroid cells, yielding an average purity of 85–95% [10]. The resulting enriched cell population is commonly referred to as CD71+ erythroid cells or CECs. However, it is imperative to exercise caution when applying this enrichment method under conditions associated with T-cell activation [19, 20] and/or acute lymphoid and myeloid leukemia, as these cells may also express CD71, greatly contaminating the isolated erythroid cell population [21].
Other erythroid cell markers, that are truly specific to erythroid cells are sialoglycoproteins Glycophorin A (CD235a) [22] and Glycophorin B (CD235b) [23], and ALAS2 –an erythroid-specific heme synthesis enzyme [24]. Mitoferrin 1 (SLC25A37 gene)–an iron-transporting protein, essential for heme synthesis [25], can also be used as a marker of erythroid cells, albeit it is not erythroid-specific and can only be treated as an enrichment marker among other more specific markers like ALAS2.
In this investigation, we undertook a comprehensive analysis of the transcriptome of healthy adult human CD235a-positive erythroid cells (Erythroid cells) utilizing the NanoString method for bulk RNA profiling, conducted an in-depth analysis of our previously published CD235a-positive adult healthy human bone marrow erythroid cell single-cell RNA sequencing (scRNA-seq) data, employing modern single-cell analysis, as well as probed the secretion of cytokines, chemokines, and TGF-beta proteins of Erythroid cells using the Bio-Plex platform (Fig 1).
This figure was created via BioRender.
We aimed to unveil the spectrum of genes encoding for the immunoregulatory cytokines, enzymes, and surface proteins expressed by healthy adult human Erythroid cells.
Results
Human bone marrow Erythroid cells display a Myeloid-like gene expression signature
We performed a bulk immune transcriptome study of healthy adult human bone marrow Erythroid cells obtained by positive magnetic sorting for CD235a via the NanoString method (n = 4) to study the involvement of such cells in immunity. Genes with detected expression (sorted in the descending mean detected probe count order) were: TFRC, LGALS3, CD36, B2M, NFKBIA, TAL1, PSMD7, C14orf166, CD59, PSMB7, ILF3, SLC2A1, C1QBP, MCL1, ITGB1, IL8 (CXCL8), MAP4K4, CD164, PSMC2, CD99, MAPK1, CD58, CTSG, TP53, FKBP5, CD44, FCGRT, MAPK14, JAK2, PSMB5, ITGA4, CTNNB1, RAF1, CD46, ARG1, CASP2, STAT5B, NFATC3, CCND3, BAX, BCL10, CCRL2, MBP, ATG5, CHUK, ARHGDIB, CD81, STAT3, CXCR4, CR1, HLA-A, CASP3, HLA-B, TYK2, S100A9, NFKBIZ, STAT5A, IRF1, CD74, MIF, IL6ST, S100A8, HLA-C and CEACAM6.
The most abundant expression-wise genes show a conservative erythroid signature: TFRC, LGALS3, CD36, and TAL1 [4, 9] which marks the high purity of the cells (Fig 2A).
(A) Heatmap of NanoString Human Immunology V2 panel genes expressed by the adult human bone marrow Erythroid cells (Eb BM). The heatmap shows log2-transformed values of the mean detected probe count, sorted in descending order; the yellow color corresponds to the maximum expression, and the deep purple color corresponds to the absence of expression (n = 4). (B) Gene Ontology Biological Process overrepresentation analysis of the genes with the detected expression in human adult bone marrow Erythroid cells. The yellow color corresponds to the lowest q-value, the deep purple color corresponds to the highest q-value, and the fullness of the bubble reflects the percentage of genes in the analysis from the full set of genes in the Gene Ontology Biological Process database.
We only detected IL8 (CXCL8) and MIF chemokine gene expression among all the cytokines and chemokines included in the NanoString V2 Immunology panel.
ARG2 (Arginase 2), CD274 (PD-L1), IL10 (IL-10), PDCD1 (PD-1), and TGFB1 (TGF-β1) gene expression was not detected.
We then performed Gene Ontology Biological Process overrepresentation analysis on the NanoString Human Immunology V2 panel genes with the detected expression in human adult bone marrow Erythroid cells and observed a “Response To Lipid” gene expression signature in the adult human bone marrow Erythroid cells (Fig 2B, Table 1).
This signature was comprised of STAT5B, STAT3, CTNNB1, CD36, JAK2, S100A8, and S100A9 genes. Among these genes, S100A8 and S100A9, antimicrobial immunity genes, were of particular interest. These genes were the dominant genes expression-wise in murine erythroid cells profiled with a similar NanoString Mouse Immunology V1 panel [4]. But, unlike in murine erythroid cells, S100A8 and S100A9 gene expression were not as high in adult human bone marrow Erythroid cells, which suggests that only a fraction of adult human bone marrow Erythroid cells could express these genes.
A subpopulation of Erythroid cells displays a myeloid gene expression signature similar to that of classic monocytes
Next, we decided to perform an advanced analysis of our previously published healthy adult human bone marrow Erythroid cell single-cell RNA sequencing (scRNA-seq) data [9]. The scRNA-seq data (n = 3) was generated using Erythroid cells from the same 3 out of 4 donors in the aforementioned NanoString analysis and using the same separation method (CD235a-positive magnetic separation) and had high gene expression correlation with the NanoString data (R = 0.8). We performed UMAP dimensionality reduction and clustering of Erythroid cells and found all stages of erythroid cell differentiation: proerythroblasts (Pro Eb), basophilic erythroblasts (Baso Eb), polychromatophilic erythroblasts (Poly Eb), orthochromatophilic erythroblasts (Ortho Eb), as well as ARG1 gene expressing orthochromatophilic erythroblasts (ARG1+ Ortho Eb) and two previously undetected subpopulations– DEFA3+ Erythroid cells (DEFA3+ Eb) and the population that we predicted from the bulk NanoString data—S100A9 expressing Erythroid cells that we termed Myeloid-like Erythroid cells (Myeloid-like Eb) (S100A8 gene was not included in the scRNA-seq panel) (Fig 3A and 3C).
(A) UMAP plot of the clusters, each cluster is color-labelled; (B) stacked bar plot of the percentages of Erythroid cells per cluster; (C) Feature plots of the Erythroid cell cluster-defining genes, the grey color represents the absence of the marker expression whereas the deep blue color represents the maximum of the marker expression; (D) dot plot that shows cluster-specific gene expression signatures, mean marker expression values were Z-score transformed, the deep purple color represents the lowest marker expression whereas the yellow color represents the maximum of the marker expression, dot size represents the percentage of Erythroid cells positive for the marker; (E) dot plot that shows Myeloid-like Erythroid cell cluster-specific gene expression signature in Myeloid cells (n = 4) and Myeloid-like Erythroid cells (n = 3), mean marker expression values were Z-score transformed, the deep purple color represents the lowest marker expression whereas the yellow color represents the maximum of the marker expression, dot size represents the percentage of erythroid cells positive for the marker.
DEFA3+ Erythroid cells comprised 2% of the bone marrow Erythroid cells while Myeloid-like Erythroid cells comprised 1% of the bone marrow Erythroid cells (Fig 3B).
Both DEFA3+ Erythroid cells and Myeloid-like Erythroid cells had the gene expression of the hallmark erythroid gene ALAS2 [24] and erythroid-enriched gene SLC25A37 [25] and had no gene expression of any myeloid marker present in the scRNA-seq panel (AZU1, CD14, FCGR3A (CD16)) [26, 27]. They also had the subpopulation-unique DEFA3 (DEFA3+ Erythroid cells) and BCL2A1, NAMPT, S100A12, IFITM2, FTH1, BTG1, CXCR4, CXCL8, CD63, DUSP1, FCER1G, S100A9, FOSB, and FCN1 (Myeloid-like Erythroid cells) gene expression respectively (Fig 3D and 3E).
The expression of the CD274 (PD-L1), IL10, PDCD1 (PD-1), and TGFB1 genes was not detected in the adult human bone marrow Erythroid cell scRNA-seq data, which is in accord with the NanoString data (See Fig 2). The ARG2 gene was not included in the scRNA-seq panel.
We then compared the gene expression of Myeloid-like Erythroid cells with other myeloid cells–Classic, Intermediate, and Non-classic monocytes and Neutrophils from the healthy adult bone marrow that we obtained from the pre-clustered publicly-available scRNA-seq data and found that Myeloid-like Erythroid cell gene expression signature resembles that of Classic monocytes the most among myeloid cells (Fig 3E) and that Myeloid-like Erythroid cells express IFITM2, BCL2A1, CXCR4, NAMPT, S100A12, FTH1, BTG1, CD63, CXCL8, HLA-A, DUSP1, FCER1G, S100A9, FOSB, and GAPDH genes on the same level as Classic monocytes (Table 2).
We then performed Gene Ontology Biological Process overrepresentation analysis on the genes with the detected expression in Myeloid-like Erythroid cells–ALAS2, SLC25A37, SNCA, YBX3, LGALS3, IFITM2, BCL2A1, CXCR4, NAMPT, S100A12, FTH1, BTG1, CD63, CXCL8, HLA-A, DUSP1, FCER1G, S100A9, FOSB, FCN1, and GAPDH and observed an overrepresentation in the “Neutrophil Chemotaxis”, “Antimicrobial Humoral Response Mediated By Antimicrobial Peptide” and “Defense Response To Fungus” terms (Fig 4, Table 3).
The yellow color corresponds to the lowest q-value, and the deep purple color corresponds to the highest q-value, and the fullness of the bubble reflects the percentage of genes in the analysis from the full set of genes in the Gene Ontology Biological Process database.
Human bone marrow Erythroid cells secrete CCL22, CCL24, CXCL5, CXCL8, and MIF chemokines
Then, we studied the secretion of cytokines and chemokines in the 24h in vitro-cultured conditioned media from healthy adult human bone marrow Erythroid cells using the BioPlex mass secretomic method (n = 6). We also decided to confirm the absence of production and secretion of TGF-beta family proteins by the same method.
We found secretion of the chemokines CCL22, CCL24, CXCL5, CXCL8, and MIF in the conditioned media from adult human bone marrow Erythroid cells. We have previously found CXCL5 gene expression in the adult human bone marrow erythroid cells (this gene was not included in the NanoString Human Immunology panel) [9], CXCL8 (IL8) and MIF gene expression was observed in the NanoString data (See Fig 1) and CXCL8 (IL8) was unique to the Myeloid-like Erythroid cells (See Fig 3D). CCL22 and CCL24 genes were absent from both NanoString and scRNA-seq gene panels. We detected no production of IL10 and TGF-β1, TGF-β2, and TGF-β3 proteins, confirming the data from both transcriptomic analyses (Fig 5A).
(A) Heatmap of cytokines secreted by human bone marrow Erythroid cells (Eb BM). The heatmap shows log2-transformed values of cytokine concentrations in pg/mL, the yellow color corresponds to the maximum detected protein secretion, and the deep purple color corresponds to the absence of protein secretion (n = 6). (B) Gene Ontology Biological Process overrepresentation analysis of the cytokines with the detected secretion in human adult bone marrow Erythroid cells. The yellow color corresponds to the lowest q-value, and the deep purple color corresponds to the highest q-value, and the fullness of the bubble reflects the percentage of proteins in the analysis from the full set in the Gene Ontology Biological Process database.
We then performed Gene Ontology overrepresentation analysis of the detected chemokines—CCL22, CCL24, CXCL5, CXCL8, and MIF, and observed enrichment in the neutrophil and eosinophil chemotaxis Gene Ontology terms (Fig 5B, Table 4).
Discussion
In this work, we performed transcriptomic and secretomic studies of adult human bone marrow Erythroid cells and were able to narrow down the spectrum of both expressed genes and secreted cytokines. We have found a gene expression signature in Erythroid cells using NanoString that included the antibacterial genes S100A8 and S100A9, and a chemokine gene CXCL8 (IL8), which we later mapped to a subpopulation of Erythroid cells we termed Myeloid-like Erythroid cells. These cells had detected expression levels of their genes similar to those of classic monocytes and gene expression signatures of antimicrobial immunity and neutrophil chemotaxis, which suggests Myeloid-like Erythroid cells as potential players in the antimicrobial immunity that could also recruit neutrophils to their site. This could be useful in case of bone marrow bacteremia, a highly lethal condition, that can happen during bone marrow transplantation [28]. Unlike erythroid-derived myeloid cells (EDMCs) [18], Myeloid-like Erythroid cells did not express any immunosuppressive genes like ARG1, CD274 (PD-L1), C10orf54 (VISTA), PDCD1 (PD-1), IL10, or TGFB1, which suggests multiple different populations of erythroid cells with myeloid-like properties. Chemokine receptor CXCR4 gene expression was unique to the Myeloid-like Erythroid cells which might restrict them to the bone marrow, as bone marrow stromal cells secrete CXCL12 –the ligand of CXCR4 [29], so we do not expect to observe Myeloid-like Erythroid cells outside of the bone marrow, therefore the expected antimicrobial protection function is only anticipated locally as well.
As all Myeloid-like Erythroid cells were positive for the S100A9 gene and were CD235a-selected, a combination of S100A9 gene expression and CD235a protein expression might be enough to find this population using the flow cytometry.
It is also puzzling to identify the stage of differentiation of Myeloid-like Erythroid cells. Myeloid-like Erythroid cells had very low levels of ALAS2 and SLC25A37 gene expression and their cluster was outside of the main branch of differentiation. This can either mean that these cells were at a very early stage of differentiation, or had left the main erythroid continuum.
As we have observed similar levels of S100A8 and S100A9 gene expression in our NanoString data, we suppose that the S100A8 gene expressed in the Myeloid-like Erythroid cells as well and forms a S100A8 / S100A9 dimer–antimicrobial protein Calprotectin, as it was observed in murine erythroid cells [4].
In our Bio-Plex secretomic analysis of the healthy adult human Erythroid cell conditional media we have found the secretion of CCL22, CCL24, CXCL5, CXCL8, and MIF chemokines. This spectrum of chemokines could allow Erythroid cells to attract neutrophils and eosinophils, which could help Erythroid cells restrict the aforementioned cell types to the bone marrow in normal condition or initiate their migration to a site of extramedullary erythropoiesis. Murine erythroid cells also had gene expression of many chemokines (yet different—Mif, Ccl2, Ccl3, Ccl9, and Cxcl12) [4], which makes chemokine secretion feature evolutionary conservative among erythroid cells. Detected CXCL8 gene expression was unique to the Myeloid-like Erythroid cells which means that this small subpopulation is solely responsible for all of the Erythroid cell production and secretion of CXCL8 (IL8).
We did not detect any IL10 gene expression in healthy adult bone marrow Erythroid cells as it was previously described [17]. This shows the importance of using newer methods for the validation purposes of some of the older studies and also means that healthy adult bone marrow Erythroid cells cannot cause IL-10-based immunosuppression in the bone marrow or at the site of extramedullary erythropoiesis.
Lack of the immunosuppressive TGFB1 gene expression differentiates human Erythroid cells from murine erythroid cells, where Tgfb1 was one the most expressed genes overall when profiled by the similar NanoString Mouse Immunology V1 panel [4], which suggests different immunosuppressive potential for human and mouse erythroid cells.
Healthy adult bone marrow Erythroid cells also differ from the tumor-induced Erythroid cells [6, 18], as they do not express any PDCD1 (PD-1) or CD274 (PD-L1) genes, which suggests restricted immunosuppression by healthy adult bone marrow Erythroid cells compared with the tumor-induced ones.
Materials and methods
Human bone marrow collection
We obtained bone marrow samples from both male (n = 3) and female (n = 3) healthy donors. The study subjects were between the ages of 23 and 28 without any underlying conditions and without any clinical evidence of anemia (n = 6). Bone marrow collection from healthy adult donors was approved by the local ethics committee of the Research Institute of Fundamental and Clinical Immunology at meeting No. 129, held in February 2021. We obtained written informed consent from all adult bone marrow donors involved in the study. The recruitment period for this study began on 01.03.2021 and ended on 30.11.2023.
Cell isolation
We collected the bone marrow aspirates (up to 5 mL in volume) into tubes containing EDTA. We isolated bone marrow mononuclear cells using density gradient centrifugation (Ficoll-PaqueTM (Thermo Fisher Scientific, Waltham, MA, USA) with a density of 1.077 g/mL) at 266 RCF for 30 min to remove RBCs.
Magnetic separation
We performed magnetic separation of the bone marrow mononuclear cells using a magnetic stand, a magnet (Miltenyi Biotec, 130-042-102, Bergisch Gladbach, Cologne, Germany), and CD235a MicroBeads (Miltenyi Biotec, 130-050-501, Bergisch Gladbach, Cologne, Germany) according to the manufacturer’s protocols.
Viability staining
We measured the CD235 magnetically sorted erythroid cell viability on a Countess 3 Automated Cell Counter (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocols using trypan blue. Trypan blue staining showed >94% viability of the Erythroid cells.
Flow cytometry data acquisition
We washed 2*106 Erythroid cells in PBS containing 0.09% NaN3 and stained surface proteins with the BioLegend (San Diego, California, United States) antibodies: #334114 PerCP/Cyanine5.5 anti-human CD71 and #349104 FITC anti-human CD235a (Glycophorin A) antibodies according to the manufacturer’s protocols. We then washed the cells after 30 minutes of incubation in the dark with 0,5 ml PBS containing 0.09% NaN3. We then added BioLegend #423113 Zombie Violet™ to all samples.
We manually gated cells from debris, singlets from the cells, alive cells from the singlets, and, finally, CD235-positive cells from the living cells in Attune NxT flow cytometer gating software. We observed > 99.0% purity of Erythroid cells (n = 6) and calculated cell count as events per uL (Fig 6).
Cell culturing
We cultured the Erythroid cells in X-VIVO 10 serum-free medium (Lonza, Basel, Switzerland) with the addition of Insulin-Transferrin for 24 h at a concentration of 1 million per mL of the medium to support their viability and measure the culture medium’s cytokines afterward.
Cell culture medium harvesting
We separated the Erythroid cell culture medium from the Erythroid cells after 24 h of culturing. We performed the separation by centrifugation at 1500 rpm for 10 min, transferred the cell culture medium into new 1.5 mL tubes with the addition of BSA up to a total concentration of 0.5%, and froze the cell culture medium at −80°C until the cytokine quantification.
Cytokine quantification in culture medium
We used 50 μL of the bone marrow Erythroid cell (n = 6) culture media to quantify culture medium cytokines, chemokines and TGF-beta proteins in doubles using a Bio-Plex Pro™ Human Chemokine Panel, 40-Plex (BioRad, #171AK99MR2, Hercules, CA, USA), Bio-Plex Pro TGF-β 3-plex Assay (BioRad, #171W4001M, Hercules, CA, USA) on a Bio-Plex 200 instrument (BioRad, Hercules, CA, USA). We then log2-transformed the data frame, created a heatmap via bioinfokit [30] library for Python 3 of the cytokines, and performed gene ontology biological process overrepresentation analysis of the detected cytokines via GSEApy [31] library for Python 3.
Total RNA extraction
We isolated total RNA from the bone marrow Erythroid cells (n = 4) after their magnetic separation and before culturing using a Total RNA Purification Plus Kit (Norgen Biotek, 48400, Thorold, Canada), measured the concentration of the RNA on a NanoDrop 2000c (Thermo Fisher Scientific, Waltham, USA) and diluted the RNA to a concentration of 10 ng/μL using nuclease-free water. We froze the diluted total RNA at −80°C until the immune transcriptome profiling.
Immune transcriptome profiling by NanoString
We performed gene expression profiling with the help of the NanoString nCounter SPRINT Profiler analytical system using 100 ng of total RNA from each Erythroid cell sample (n = 4). We used a nCounter Human Immunology v2 panel to analyze the total RNA samples. The nCounter Human Immunology v2 panel consists of 579 immunity-associated genes, 15 housekeeping genes, and eight negative and six positive controls. The samples were subjected to a 20h hybridization reaction at 65°C, where 5–12 μL of total RNA was combined with 3 μL of nCounter Reporter probes, 0–7 μL of DEPC-treated water, 10 μL of hybridization buffer, and with 5 μL of nCounter capture probes (total reaction volume = 30 μL). After the 20h hybridization of the probes to targets of interest in the samples, the number of target molecules was determined on the NanoString nCounter SPRINT Profiler analytical system. We performed normalization and QC in nSolver 4 using added synthetic positive controls and the RPL19, OAZ1, GAPDH, EEF1G, TUBB, and HPRT1 housekeeping genes included in the panel. We then performed background thresholding on the normalized data to remove non-expressing genes. The background level was determined as the mean of the POS_E controls and the genes that did not pass the threshold in at least one sample were removed. We manually added the threshold of detection (the mean of the POS_E controls) and ARG2, CD274, IL10, and TGFB1 genes that were below the threshold of detection to the data frame. We then log2-transformed the data frame, created a heatmap via bioinfokit [30] library for Python 3 of the detected genes, and performed gene ontology biological process overrepresentation analysis of the detected genes via GSEApy [31] library for Python 3.
Single-cell RNA-seq data analysis via Seurat
We analyzed our previously published publicly-available adult bone marrow CD235a-positive erythroid cell scRNA-seq data (GSE199230) via Seurat [32]. scRNA-seq data set was prepared using erythroid cells from the same donors (3/4) and using the same separation method (CD235a-positive magnetic separation) as was the NanoString assay in this paper and had high gene expression correlation with the NanoString assay (R = 0.8012). We tested the log10-transformed gene expression correlation using Pearson correlation in the GraphPad Prism 10.2.3. We subjected the expression matrix to a quality control procedure, found the most variable genes in expression for the merged matrix via FindVariableFeatures, performed SCTransform V2 data normalization using the variable features on the merged data, performed PCA (principal component analysis) dimensionality reduction on the normalized data, performed the UMAP dimensionality using 20 principal components. Then, we found Erythroid cell clusters that corresponded to the subsequent stages of erythroid cell differentiation. Clusters we identified using ALAS2, CD36, and ITGA4 genes–ALAS2, SCL25A37, and SCNA gene expression gradually increased from the proerythroblasts stage and on, CD36 and ITGA4 gene gradually decreased from the proerythroblasts stage, ARG1+ Erythroid cells had the unique expression of ARG1 gene. We also found two small subpopulations of erythroid cells– DEFA3+ and Myeloid-like Erythroid cells. Then, we prepared SCT markers for the differential gene expression testing via PrepSCTFindMarkers and performed inter-cluster differential gene expression using the Wilcoxon test with biological and statistical significance criteria of log2(Fold Change) > 0.847 or log2(Fold Change) < −0.847 and q-value < 0.005 via FindMarkers and created the dot plot of the differentially expressed genes via DotPlot in Seurat.
For the comparative analysis with myeloid cells, we exported Myeloid-like Erythroid cell raw data, downloaded the gene expression matrix of the normal bone marrow mononuclear cells (n = 4) obtained with the same technology and gene panel (BD Rhapsody, Immune Response Panel) from the Gene Expression Omnibus (GSE261733, https://github.com/RIFCILab/ALL-BM-scMultiomics, accessed on 17.03.2024), exported pre-clustered Classic, Intermediate and Non-Classic Monocyte and Neutrophil raw data, merged the Myeloid-like Erythroid cell, Neutrophil, Classic, Intermediate and Non-Classic Monocyte raw data in Seurat via Merge, found the most variable genes in expression for the merged matrix via FindVariableFeatures, performed SCTransform V2 data normalization using the variable features on the merged data, prepared SCT markers for the differential gene expression testing via PrepSCTFindMarkers, performed differential gene expression testing using the Wilcoxon test with biological and statistical significance criteria of log2(Fold Change) > 0.847 or log2(Fold Change) < −0.847 and q-value < 0.005 via FindMarkers, created the dot plot of the differentially expressed genes via DotPlot.
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