Proteomic profiling of human HSPCs from fetal to adult stages

We first investigated the differences in the protein characteristics of human HSPCs by generating single-cell suspensions from human FLs of 9–10 PCW, UCB, and aBM samples (Fig. 1A). MNCs from FL, UCB, and aBM were enriched by Ficoll density gradient separation. CD34 is a well-known common marker of human HSPCs, as validated by human clinical transplantation and xenograft transplantation assays [27]. CD34+ HSPCs are still a mixture of heterogeneous populations that contain CD34+CD38− noncommitted progenitors (HSCs and multipotent progenitors [MPPs]) and CD34+CD38+ lineage-committed progenitors. Moreover, only a small minority of CD34+ cells are true HSCs [28]. We subsequently analyzed the composition of HSPCs and HSC frequency at different developmental stages (Fig. 1B, Additional file 1: Fig. S1A-C). The percentages of lin−CD34+CD38− cells in FL, UCB, and aBM among total live cells were 0.65 ± 0.13%, 0.18 ± 0.05%, and 0.05 ± 0.01%, respectively, whereas the percentages of lin−CD34+CD38+ cells were 1.43 ± 0.30%, 0.36 ± 0.08%, and 0.93 ± 0.23%, respectively (Fig. 1C/1D). The proportions of human FL, UCB, and aBM HSCs (Lin−CD34+CD38−CD90+CD45RA−) [29, 30] were 0.14 ± 0.04%, 0.02 ± 0.002%, and 0.01 ± 0.006%, respectively (Additional file 1: Fig. S1D), which are capable of long-term multilineage transplantation according to previous studies.

Fig. 1

Proteomic profiling of human HSPCs at different developmental stages. A Schematic diagram of the experimental design. Lin−CD34+ HSPCs were derived from human FL, UCB, and aBM stages. The original proportion of Lin−CD34+CD45mid HSPCs was analyzed via FACS. The MACS enrichment efficiency was confirmed by FACS again after sorting. The proteome of human Lin−CD34+ HSPCs was subsequently analyzed. Finally, relevant functional verification, including Seahorse, RT‒PCR, metabolic-related assays, and cell culture assays, were conducted. B Representative flow cytometry profiles of FL, UCB, and aBM cells plotted as hCD34 and hCD38 from live, single, lineage negative cells. C Percentages of Lin−CD34+CD38−/Lin−CD34+CD38+ cells among live FL, UCB, and aBM cells (FL, n = 4; UCB, n = 4; aBM, n = 3). The error bars represent the mean ± SD; the significance is indicated by paired Student’s t tests. *p < 0.05. D Percentages of the CD38− and CD38+ fractions in FL, UCB, and aBM Lin−CD34+ cells (FL, n = 4; UCB, n = 4; aBM, n = 3). The error bars represent the mean ± SD. E Flow cytometry analysis before and after CD34 enrichment in FL, UCB, and aBM CD34+CD45mid HSPCs. F Overview of the protein numbers of FL, UCB, and aBM HSPCs identified via DIA. G Venn diagram of proteins identified in FL (brown), UCB (dark grey), and aBM (peacock blue) samples. H PCA for the indicated comparison using all quantified proteins of FL (brown), UCB (dark grey), and aBM (peacock blue). PCA1 and PCA2 denote the first and second principal components, respectively.

To obtain enough CD34+ HSPCs with high purity, the MNCs from FL, UCB, and aBM were magnetically enriched for CD34+ cells using the human CD34 microbeads kit [24]. Then, Lin−CD34+ HSPCs were sorted after they were Ficoll-purified and CD34-enriched (Fig. 1E). The expression of the CD45 antigen in lineage blood cells is higher than that in HSCs/HPCs [24]. We also confirmed that Lin−CD34+ HSPCs derived from Ficoll-purified CD34+ MNCs were restricted to HSPCs and excluded from CD34+CD45− endothelial cells (ECs) and lineage-positive (CD2, CD3, CD14, CD16, CD19, CD56, and CD235a) cells [31, 32]. Giemsa staining also confirmed that the CD34+CD14− population presented with typical blast/stem cell characteristics of a relatively high nucleus-to-cytoplasm ratio [31]. We generated the proteome data from FL (n = 4), UCB (n = 5), and aBM (n = 4) samples (105 Lin−CD34+ cells per sample).

Between 4,739 and 6,499 proteins (an average of 5,730 proteins per cell) were robustly quantified for each sample by data-independent acquisition (DIA) (Fig. 1F). Compared with previously published studies on human aBM HSPCs, the protein numbers identified in our datasets were comparable [18, 21]. The number of overlapping proteins identified among the three cell populations was 4,655 (72.68%) (Fig. 1G, Additional file 5). In addition, 411 proteins (6.42%) were exclusively detected in FL HSPCs (defined as “fetal-specific”), 106 proteins (1.65%) were identified as “newborn-specific”, and only 10 proteins (0.16%) were “adult-specific”. As expected, principal component analysis (PCA) revealed clear segregation between HSPCs at distinct developmental stages (Fig. 1H). Pearson correlation coefficient analysis between biological replicates in each of the three stages yielded, on average, r = 0.863 (± 0.058), indicating consistent measurement and high reproducibility of the whole workflow (Additional file 1: Fig. S2A).

Subsets of human HSPC proteins undergo fetal-to-adult transition with different patterns

Next, we calculated the DEPs between HSPCs at different developmental stages to identify their protein characteristics (fold change > 1.5 or < 0.667 and p < 0.05) (Fig. 2A, Additional file 1: Fig. S2B, Additional file 3). The subcellular distribution of DEPs was shown in Additional file 1: Fig. S2C. These DEPs were used for downstream analyses. Statistical analysis revealed 1,358 DEPs between fetal and newborn HSPCs. The most significant fraction (1,088 proteins) of these proteins was highly expressed in FL HSPCs. Surprisingly, compared with FL or aBM HSPCs, UCB HSPCs presented very few DEPs, which suggested that UCB HSPCs presented more similar protein expression features to FL and BM HSPCs but fewer unique expression features. The proteins differentially expressed between different pairs of cell populations were analyzed via a Venn diagram (Fig. 2B, Additional file 1: Fig. S4A–B, Additional file 5). To identify the dynamic changes in HSPC development between neighbouring cell populations, fuzzy c-means (FCM) [33] was applied to cluster global protein expression profiles. We observed nine distinct clusters of protein expression patterns representing proteins that were differentially regulated (Fig. 2C, Additional file 1: Fig. S3, Additional file 4). Among these, clusters 4 and 6 represented proteins upregulated in fetal HSPCs and then downregulated after birth. In contrast, clusters 5 and 7 represented proteins that were downregulated in fetal HSPCs and upregulated after birth.

Fig. 2

Differential protein levels throughout human HSPC development. A Changes in the proteomics of upregulated and downregulated proteins between each pair in FL, UCB, and aBM HSPCs. DEPs were analyzed at thresholds of FC > 1.5 or FC < 0.67 and p < 0.05. B Overlapping DEPs between each pair of stages: FL vs. UCB, UCB vs. aBM, and FL vs. aBM. C Mfuzz analysis is divided into nine different expression trends on the basis of the expression trends of all DEPs of each stage for human HSPCs. The X-axis represents the other groups, and the Y-axis represents the expression change after homogenization. The lines of each cluster refer to a class of proteins expressed in proteins. Each cluster identifies its representative proteins. D Heatmap showing the -log10 transformed significant value (p value) of the KEGG pathway terms describing each of the 9 clusters. E KEGG pathway enrichment analysis of terms significantly upregulated and downregulated in fetal HSPCs versus UCB and aBM HSPCs

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was subsequently applied to identify pathways enriched in each cluster. Notably, fatty acid degradation, oxidative phosphorylation (OXPHOS), the tricarboxylic acid (TCA) cycle, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, and ribosome were enriched in clusters 4 or 6, with higher expression in fetal HSPCs (Fig. 2D–E). In contrast, chemokines, immune-related pathways (such as T and B-cell receptors), signalling pathways (Wnt, MAKP, TGF-β, and FoxO), antigen processing and presentation, proteasome, spliceosome (involved in mRNA maturation), glycolysis/gluconeogenesis, and the pentose phosphate pathway (PPP) were among the top differentially regulated pathways enriched in clusters 5 or 7, showing an upregulated pattern during human HSPC development (Fig. 2D–E). The FoxO signalling pathway, which plays essential roles in response to physiological oxidative stress and the detoxication of ROS [34], was active in UCB and aBM HSPCs but not in FL HSPCs (Fig. 2D).

Protein translational machinery-related pathways, such as ribosome and ribosome biogenesis, were more enriched in FL HSPCs than in UCB and aBM HSPCs (Fig. 2D–E, Additional file 1: Fig. S3A). Both ribosomal assembly and protein synthesis have been implicated in HSC function and regeneration [26, 35]. A low level of protein synthesis is essential for maintaining HSC metabolic homeostasis, whereas an alteration in protein synthesis impairs HSC function [26, 36]. Homeostatic or quiescent HSCs restrict protein synthesis for long-term maintenance. In contrast, proliferating HSCs undergo excessive protein synthesis to support their drastic expansion, which may increase the number of misfolded proteins and impair HSC function. Human FL hematopoiesis requires a highly regulated protein synthesis rate; it acts as an essential niche for hematopoietic differentiation and HSC expansion until birth [1, 32]. In addition, peroxisome-related proteins were also upregulated in FL HSPCs, whereas the spliceosome and proteasome were highly expressed in UCB and aBM HSPCs (Fig. 2D–E, Additional file 1: Fig. S3A).

Taken together, our proteome data revealed dynamic and complex protein expression patterns during the fetal-to-adult transition process in human HSPC development, which occurred progressively along with a continuum of HSPC maturation.

Metabolic switch of human HSPCs from fetal to adulthood through the perinatal period

Compared with HSPCs in the UCB or aBM stages, those in the FL stage proliferate extensively, resulting in different metabolic demands [24]. However, the metabolic pathways responsible for the production of energy and protein synthesis at the human fetal, newborn, and adult stages have not been described comprehensively. As shown in Fig. 2E and Additional file 1: Fig. S3B, the most notable proteome differences between HSPCs at the three different developmental stages were related to central carbon metabolism (summarized in Fig. 3). Proteome analysis revealed that the rate-limiting enzymes involved in facilitating the entry of glucose into glycolysis/gluconeogenesis, such as hexokinase 1 (HK1), glycogen phosphorylases liver form (PYGL), phosphoglucose mutase 2 (PGM2), glycolytic enzymes aldolase C (ALDOC), triosephosphate isomerase (TPI1), and enolase-1/2 (ENO1/2), were overexpressed in UCB or aBM HSPCs (Fig. 3A). These findings indicated that adult HSPCs rely primarily on glycolysis to supply energy. HK1, the enzyme that catalyses the first rate-limiting step of glycolysis, exhibited an age-associated increase among human BM CD34+ HSPCs [20]. In addition, we detected a significantly increased level of ENO1 in UCB HSPCs compared with their FL counterparts via RT‒PCR (Fig. 3B). The quiescent HSPCs have low energy requirements and are believed to depend on glycolysis rather than mitochondria. They are sustained by a hypoxic ecological niche and display great self-renewal capacity [37,38,39].

Fig. 3

Prominent changes in central carbon metabolism occur in HSPCs at three developmental stages. A–B Glucose metabolism (glycogen metabolism and PPP) A and the TCA cycle B in FL (brown), UCB (dark grey), and aBM (peacock blue) HSPCs are depicted as unidirectional arrows representing unidirectional reactions and bidirectional arrows indicating bidirectional reactions. The gene names of the respective enzymes are written in capital letters. Green indicates the enzymes upregulated in fetal HSPCs, red indicates the enzymes downregulated in fetal HSPCs, and black indicates which enzyme was not changed (see also Fig. 4). Fold change (FC) proteomics expression data of enzymes are shown as histograms in the indicated comparisons. Four to six independent experiments were performed per enzyme. The significance of the proteomic data is indicated by one-way ANOVA with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. C–D RT‒PCR analysis (n = 3) of ENO1 and SDHB in FL and UCB HSPCs. The significance is indicated by an unpaired Student’s t test. *p < 0.05 and **p < 0.01

In contrast, the proteins involved in OXPHOS and the TCA cycle presented increased expression in FL HSPCs (Fig. 3C). Pyruvate dehydrogenases (PDHB and PDHX) were highly expressed in FL HSPCs, promoting entry into the TCA cycle [40]. Furthermore, FL HSPCs presented increased pyruvate levels, although the difference was insignificant (Fig. 4G), consistent with increased mitochondrial OXPHOS. The enzymes involved in the TCA cycle (such as ACO2, IDH1, SDHA, SDHB, and FH) were abundant at the fetal stage (Fig. 3C-D). FH has been demonstrated to be a critical metabolic regulator of HSC self-renewal and differentiation [41]. These significantly differentially expressed enzymes could be targeted to intervene in the energy metabolism of human HSPCs and further influence their fate.

Fig. 4

The metabolic landscape of human HSPCs among FL, UCB, and aBM. A Changes in purine, AA, fatty acid, ketogenesis, glutamine, and glutathione metabolism between FL (brown), UCB (dark grey), and aBM (peacock blue) HSPCs. Green indicates the enzymes upregulated in fetal HSPCs (see Fig. 3). Fold changes in the proteomeo data of enzymes are shown as histograms in the indicated comparison. Four to six independent experiments were performed per enzyme. The significance of the proteomic data is indicated by one-way ANOVA with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, and ***p < 0.001. B Scheme of glutamine-related metabolism and glutathione metabolism replenishment. C Proteomic expression data of glutathione metabolism-related enzymes are shown as histograms. The significance of the proteomic data is indicated by one-way ANOVA with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. D RT‒PCR analysis (n = 3) of GSTP1, LANCL1, GSTM2, and GSS in FL and UCB HSPCs. *p < 0.05, **p < 0.01, and ***p < 0.001. E GST levels of FL, UCB, and aBM HSPCs. Significance is indicated by one-way ANOVA with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. F GSH and GSSG levels in FL and UCB HSPCs. Significance is indicated by the Student’s t test. *p < 0.05, **p < 0.01, and ***p < 0.001. G Pyruvate levels in FL and UCB HSPCs as indicated (n = 3)

In addition to these mitochondria-related pathways, amino acid (AA) metabolism (especially valine, arginine, and proline), fatty acid degradation, lipoic acid metabolism, and steroid biosynthesis were among the pathways most differentially regulated in FL HSPCs compared with those in UCB or aBM HSPCs (Fig. 2D/4A). Enzymes involved in glutamine metabolism, such as glutaminase (GLS) and glutamate dehydrogenase 1 (GLUD1), were highly enriched in FL HSPCs (Fig. 4B). However, the enzymes that participate in glutathione (GSH) metabolism, such as S-transferases (GSTs), GSTP1, LANCL1, and GSTM2, as well as the GSH synthesis enzymes GSR and GPX4, presented increased expression patterns in UCB or aBM HSPCs (Fig. 4B-C). We also detected significantly increased levels of GSTP1, LANCL1, GSTM2, and GSS in UCB or aBM HSPCs compared with their FL counterparts via RT‒PCR (Fig. 4D). Differences in mRNA expression were detected mainly before and after birth, not between newborn and adult HSPCs. In addition, UCB and aBM HSPCs displayed high GST activity (Fig. 4E). Interestingly, compared with UCB HSPCs, FL HSPCs presented significantly increased levels of free glutathione pools, both reduced (GSH) and oxidized (GSSG) (Fig. 4F). We hypothesize that UCB HSPCs may have a higher glutathione transfer metabolism rate to consume GSH and GSSG than FL HSPCs, which needs further investigation.

Switching from the oxidative pathway to the glycolytic pathway coincides with the development of human HSPCs

Seahorse analysis was used to measure the oxygen consumption rate (OCR), which was indicative of OXPHOS. In contrast, the proton efflux rate (PER) in the glycolytic rate assay provides accurate measurements of glycolytic rates under basal conditions and compensatory glycolysis following mitochondrial inhibition, which indicates glycolysis. As shown in Fig. 5A-E, compared with FL HSPCs, UCB HSPCs demonstrated a lower OCR rate, basal respiration, maximal respiration, spare respiratory capacity, ATP production, and nonmitochondrial oxygen consumption. In contrast, UCB HSPCs displayed relatively greater basal glycolysis and compensatory glycolysis via PER measurements. Taken together, these findings are consistent with our findings that FL HSPCs primarily use OXPHOS to meet their energy demands and have a more active metabolic state than UCB HSPCs [11].

Fig. 5

Higher mitochondrial function and increased glucose uptake support the oxidative consumption of FL HSPCs. A OCRs were examined in FL and UCB HSPCs via the Seahorse assay. The error bars represent the mean ± SEM. B Levels of basal respiration, maximal respiration, spare respiratory capacity, ATP production, and nonmitochondrial oxygen consumption were examined. The error bars represent the mean ± SEM; the significance is indicated by unpaired Student’s t tests. *p < 0.05. C PERs of FL and UCB HSPCs were measured. The error bars represent the mean ± SEM. D–E The levels of basal glycolysis (D) and compensatory glycolysis (E) were examined. The error bars represent the mean ± SEM. F–G Flow cytometric analysis of 2-NBDG glucose uptake in Lin−CD34+CD38− (F) and Lin−CD34+CD38+ (G) cells at three developmental stages. H–I Flow cytometric analysis of total mitochondrial mass (MTG) in Lin−CD34+CD38− (H) and Lin−CD34+CD38+ (I) cells at three developmental stages. J–K Flow cytometric analysis of the active mitochondrial content (MitoTracker Red CMXRos) in Lin−CD34+CD38+ (J) and Lin−CD34+CD38− (K) cells at three different developmental stages. L–M Flow cytometric analysis of cellular ROS levels in Lin−CD34+CD38− (L) and Lin−CD34+CD38.+ (M) cells at three developmental stages. Significance is indicated by one-way ANOVA with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001

As the proteomic profile and Seahorse analysis revealed an increased mitochondrial respiratory rate in fetal HSPCs, we investigated whether human HSPCs at different developmental stages differed in terms of mitochondrial content and activity, glucose uptake, ROS production, and ribosomal translation. To obtain a sufficient number of cells for assessing mitochondrial function, we focused on the Lin−CD34+CD38− population. MTG and MitoTracker Red CMXRos were used to quantify the total and active mitochondria, respectively (Fig. 5F/5H, Additional file 1: Fig. S5A-B). We also measured glucose uptake using 2-NBDG, a fluorescently tagged glucose analogue (Fig. 5J). Protein synthesis was analyzed via an O-propargyl-puromycin (OP-puro) incorporation assay (Additional file 1: Fig. S5G-H). FL Lin−CD34+CD38− showed higher total and active mitochondria content than that in its UCB and aBM counterparts. An apparent increase in ROS production was noted in the FL Lin−CD34+CD38− population compared with that in the UCB and aBM populations (Fig. 5L). The level of glucose uptake also increased in the FL Lin−CD34+CD38− population. The same pattern was also shown in the Lin−CD34+CD38+ HPC population (Fig. 5G/5I/5K/5M), which was consistent with our proteomic analysis of the Lin−CD34+ HSPC population (Additional file 1: Fig. S5C-F).

Distinct response of human Lin −CD34 +CD38 − HSPCs and Lin −CD34 +CD38 +HPCs to perturb glutathione metabolism

The enzymes (GSTP1, GSTM2, and LANCL1) involved in glutathione metabolism showed increased expression patterns in human HSPCs after birth (Fig. 4C-D). We investigated whether perturbing glutathione metabolism affects ROS production, the metabolic state, and the expansion of human HSPCs (Fig. 6A). BSO, an inhibitor of glutathione synthetase, can lead to an increase in intracellular ROS levels [42]. Treatment with BSO (125 µM) in vitro for two days significantly decreased HSC expansion (Lin−CD34+CD38−), leading to differentiation and significantly increased levels of ROS (Fig. 6B/6C/6I). Next, we asked whether the pharmacological inhibition of ROS elevation could protect human HSPCs from functional degradation. The antioxidant agent N-acetyl-L-cysteine (NAC, 100 µM) protected human HSPCs from BSO-induced HSPC differentiation and elevated ROS levels (Fig. 6B/6C/6I).

Fig. 6

Distinct response of human HSCs and HPCs to disturb glutathione metabolism. A Experimental diagram of BSO/NAC/BSO + NAC treatment of human CD34+ HSPCs in vitro. B Number of total CD34+ HSPCs after culture in vitro for 48 h with BSO/NAC/BSO + NAC. C Frequency of CD34+CD38− and CD34+CD38+ cells among CD34+ HSPCs after 48 h of culture with BSO/NAC/BSO + NAC. D–E Apoptosis analysis of HSPCs cultured for 48 h with BSO/NAC/BSO + NAC. Early (Annexin V+7-AAD−) and late (Annexin V+7-AAD+) apoptosis were quantified by flow cytometry in Lin−CD34+CD38− HSPCs (D) and Lin−CD34+CD38+ HPCs (E). F Cell cycle analysis of CD34+CD38− (up) and CD34+CD38+ (down) cells among CD34+ HSPCs after 48 h of culture with BSO/NAC/BSO + NAC. G–J Flow cytometric analysis of total mitochondria (MTG) (G), active mitochondrial content (MitoTracker Red CMXRos) (H), ROS levels (I), and protein synthesis rate (OP-Puro) (J) in Lin−CD34+CD38− HSCs. (Error bars represent the means ± SEM, n > 3). Significance was indicated by one-way ANOVA with Tukey’s multiple comparison test (n > 3, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001)

Unexpectedly, the apoptosis of HSPCs (Lin−CD34+CD38−) and progenitors (Lin−CD34+CD38+) in response to BSO and NAC was significantly different (Fig. 6D-E). The increase in BSO-induced elevated ROS levels significantly increased the number of apoptotic Lin−CD34+CD38− HSPCs (FC = 20.85, p < 0.0001, relative to CON), and NAC rescued the cells from apoptosis (FC = 5.36, p < 0.0001, relative to CON) (Additional file 1: Fig. S6A). This effect appears to be specific to primitive Lin−CD34+CD38− cells, as no drastic increase in apoptosis was observed in Lin−CD34+CD38+ HPCs (FC = 3.60, p < 0.0001, relative to CON). In contrast, NAC aggravated the apoptosis of HPCs (FC = 6.05, p < 0.0001, relative to CON) (Additional file 1: Fig. S6A). Interestingly, BSO treatment decreased the total mitochondrial content while increasing the active mitochondrial content in both the HSPC and HPC populations (Fig. 6G-H, Additional file 1: Fig. S6B-C). These results were also validated by quantifying the cellular ROS levels (Fig. 6I, Additional file 1: Fig. S6D-E). BSO treatment halted the cell cycle in Lin−CD34+CD38− cells but not in Lin−CD34+CD38+ cells (Fig. 6F). BSO treatment of human CD34+ cells also significantly decreased de novo translation (protein synthesis rate), which was confirmed using OP-Puro (Fig. 6J, Additional file 1: Fig. S6F-G).

Taken together, these results demonstrated that the oxidative stress induced by BOS treatment led to ROS accumulation, a decreased protein synthesis rate, and the collective induction of apoptosis in Lin−CD34+CD38− cells. In contrast, Lin−CD34+CD38+ cells could resist apoptosis to some extent. The antioxidant NAC also induced different responses to rescue BSO-induced elevated apoptosis in HSPCs and HPCs. Glutathione metabolism and ROS play critical roles in human HSC function (especially in mitochondria), and the regulation of glutathione-related metabolism and ROS has the potential to maintain HSC function and longevity and improve HSC regeneration or HSCT in patients.

Progressive HSPC maturation progresses from the FL stage to the adult stage

The first human HSCs are generated from arterial ECs via HSC-primed HECs through EHT in the AGM region [23, 24]. Afterwards, the first transplantable HSCs in the liver are detected at 7 weeks [43]. The FL is a major site for HSC amplification and differentiation. Most human FL HSPCs (CD34+CD144+CD45+ cells) lose the expression of endothelial-specific markers as they rapidly start expressing hematopoietic markers following HSC colonization to the liver [43]. Our proteome data demonstrated that highly validated fetal-biased HSPC proteins (such as LIN28B, IGF2BP2, and IGF2BP3) and endothelial-related proteins (such as ESAM [44,45,46] and ENG [47, 48]) were overexpressed in FL HSPCs (Fig. 7A/K). However, the overexpression of HSC maturation-related proteins (PROM1 [