Bone marrow (BM) is the principal site of hematopoiesis in adult humans. In the BM, hematopoietic stem cells (HSCs) and their progenies are contained in specialized microenvironments which regulate HSC maintenance and differentiation.

Our understanding of the BM hematopoietic microenvironment (HME) has evolved considerably over the past decade through a number of landmark studies that have identified the cellular identity, anatomy, and functions of different murine HME components (Baryawno et al., 2019; Tikhonova et al., 2019; Wolock et al., 2019; Baccin et al., 2020). Skeletal and stromal cell populations, endothelial cells and other non-hematopoietic cells but also differentiated hematopoietic cells have been described as important HME elements in mice (Méndez-Ferrer et al., 2008; Urbieta et al., 2010; Yamazaki et al., 2011; Ding et al., 2012; Casanova-Acebes et al., 2013; Zhao et al., 2014; Chan et al., 2018).

In contrast, important HME elements in human BM remain poorly defined, which is due to the fact that the identity and function of the stromal stem/progenitor cells have been difficult to investigate (Li et al., 2016). Nevertheless, a number of recent studies have provided the first important insights into the complexity of the human BM and the potential diverse functional roles of BM stromal cells (Chan et al., 2018; de Jong et al., 2021; Triana et al., 2021; Wang et al., 2021).

Based on single-cell RNA sequencing (scRNAseq) technology, we herein investigated the human non-hematopoietic BM cell compartment aiming to resolve the composition of the human HME at the highest possible resolution, to identify potential novel marrow stromal subsets and cellular hierarchies as well as to establish functional relationships between stromal and hematopoietic elements.

The BM microenvironment plays a critical role in regulating hematopoiesis. Recent landmark studies have illustrated the molecular complexity of the murine BM microenvironment (Baryawno et al., 2019; Tikhonova et al., 2019; Baccin et al., 2020). However, the exact definition of the cell populations that form the BM stroma in humans remains elusive. In this study, based on single-cell RNA sequencing technology, we gained important insights into the stromal components of this important organ including their developmental and functional roles in hematopoiesis.

Several recent studies using scRNAseq approaches provided the first important insights into the complexity of the human BM and the potential functional roles of BM stromal cells (de Jong et al., 2021; Triana et al., 2021; Wang et al., 2021). A first overview of the cellular composition of the nonhematopoietic cells in human BM was provided by a recently published single-cell proteo-genomic reference map (Triana et al., 2021) and a single-cell transcriptomic map of non-hematopoietic cells (de Jong et al., 2021). However, due to the low frequencies of the stromal stem/progenitor cells, it has been difficult to investigate the potential heterogeneity of the human BM stromal cell compartment even when employing single cell approaches. Enrichment strategies represent a possible approach for a detailed analysis of the transcriptional diversity of BM stromal cells, as recently reported by Wang et al., 2021. However, in their study, CD271⁺ cells from femoral shafts from old patients with osteoarthritis and osteoporosis were used, which might not reflect transcriptomic profiles of normal stroma cells from hematologically active bone marrow (Wang et al., 2021). Because of the extremely low frequency of stromal cells in human BM, we chose a sorting strategy that also included CD45^low cells to ensure that no stromal cells were excluded from the analysis. Furthermore, we also added sorted CD271⁺ cells, which are highly and exclusively enriched for stromal stem/progenitor cells (Tormin et al., 2011). Therefore, despite the expected presence of hematopoietic cells in our dataset, this approach ensured that stromal cell resolution was substantially increased.

Following exclusion of the hematopoietic clusters, we identified primarily nine stromal clusters which could further be assigned to six stromal cell types, that is MSSCs, HAGEPs, balanced progenitors, OCs, pre-osteoblasts, and pre-fibroblasts. Of note, corresponding murine stromal cell types have been reported for most of these cell types (MSSCs, HAGEPs, OCs, pre-osteoblasts, and pre-fibroblasts), whereas the cluster annotated as balanced progenitors has not been described either in mice or in humans (Baryawno et al., 2019; Tikhonova et al., 2019).

Additionally, we identified three non-hematopoietic cell clusters which contained KRT5- (cluster 0) and neuronal gene-expressing cells (cluster 39), respectively, as well as endothelial cells. KRT5 is a keratin family member and is widely used as a basal cell marker. However, KRT5 expression has also been reported for several immune cell types, such as plasmacytoid dendritic cells and NK cells. As cells in our analysis did not express typical immune cell genes, further characterization of this cluster needs to be performed in future studies. Multiple genes indicating the presence of different cell types were identified in cluster 39. The genes identified with the highest fold changes included several neuronal markers (NEUROD1, CHGB, ELAVL2, ELAVL3, ELAVL4, STMN2, INSM1, ZIC2, NNAT) (Supplementary file 4). Due to the heterogeneity of this cell composition, this cluster was therefore annotated as neuronal cell-containing cluster.

Thus, this analysis allowed us to dissect the human bone marrow non-hematopoietic cell compartment at a thus far unreached molecular level, to resolve the stromal cell heterogeneity and generate a detailed transcriptional fingerprint of distinct stromal populations.

Furthermore, we were able to define phenotypically and functionally distinct stromal subsets, which were predicted to be organized in a hierarchical manner according to RNA velocity analysis. We identified MSSCs as the most primitive population, which is in accordance with published murine and human stromal stem cell studies (Zhou et al., 2014; Baryawno et al., 2019; Wolock et al., 2019). Pre-fibroblasts, pre-osteoblasts, and OCs were placed downstream of MSSCs in the developmental hierarchy, thus confirming the reliability of the velocity analyses. By using a likelihood-based computation, we also detected potential key driver genes that are candidates to govern the transition between different cellular states. Identified genes included both novel and established transcription factors and certainly, these data provide the basis for future important mechanistic studies of the dynamic processes associated with cellular state transition and fate choices.

Self-renewing human skeletal stem cells (hSSC) with osteogenic and chondrogenic potential have been previously identified in human fetal and adult bones based on the phenotype PDPN⁺CD146^-CD73⁺CD164⁺ (Chan et al., 2018). However, the identity of a tri-lineage multipotent stromal progenitor in adult human bone marrow was not reported. Herein, we were able to provide a phenotypical definition that allowed for the faithful identification and prospective isolation of molecularly defined stromal stem and progenitors from hematopoietically active adult bone marrow. These cells have thus far remained incompletely characterized and our data thus complement and extend reported phenotype definitions of MSCs from bone marrow and skeletal tissues (Jones et al., 2002; Quirici et al., 2002; Gronthos et al., 2007; Sacchetti et al., 2007; Delorme et al., 2008; Li et al., 2016; Chan et al., 2018). The phenotypic MSSCs defined in this study also showed expression of previously reported stromal surface markers such as CD51 (Pinho and Frenette, 2019) and CD146 (Tormin et al., 2011; data not shown). Besides these surface markers, scRNAseq also identified VCAN as a potentially important stromal gene based on its high expression in stromal clusters. However, as an extracellular matrix protein, FACS analysis of cellular VCAN expression can only be achieved based on its intracellular expression after fixation and permeabilization. Additionally, VCAN is also expressed by monocytes (cluster 36). Therefore, VCAN is not an optimal marker to isolate viable stromal cells.

Furthermore, we performed a preliminary analysis which indicated possible age- and gender-related stromal differences as also reported for murine cells (Singh et al., 2016; Maryanovich et al., 2018; Aguilar-Navarro et al., 2020). However, this potentially interesting finding needs to be confirmed by additional experimentation in forthcoming studies.

In mice, elegant studies using genetic approaches involving in-vivo labelling of cell types and ablation of candidate niche cells and niche factors have provided detailed insight into the functional roles of stromal cells in regulating hematopoiesis (Pinho and Frenette, 2019). Comparable studies in human are impossible to perform and analysis of human stromal cells function has been limited to in-vitro models. We therefore investigated cellular interactions based on ligand-receptor interactions and found a broad and complicated crosstalk network, which is likely to reflect – at least in part – the in-situ situation as data were generated with directly isolated cells. We found that stromal cells generally showed hematopoiesis supporting potential, which is consistent with previously published in-vitro coculture data (Li et al., 2014; Li et al., 2020) and which we confirmed in co-cultures with BM CD34⁺ cells and MSSC- or OC-derived stromal feeder cells (Figure 5—figure supplement 3E). Furthermore, whereas all stromal cell groups showed the potential to interact with almost all hematopoietic cells, cell-cell interaction mechanisms differed considerably between different groups of stromal cell groups, that is between group A/C cells which included the MSSCs and group B cells (OCs). Whereas the former demonstrated the potential to interact with hematopoiesis through cytokines such as CXCL12, IL7, KITLG, MDK, and TF as well as adhesion molecules providing direct cell-to-cell contact (e.g. VCAM1), SPP1-mediated interactions were predicted to be the main pathways for the latter. Although we have not formally examined the expression of the receptors for CXCL12 and SPP1 on hematopoietic cells in the current study, expressions of CXCR4 (CXCL12 receptor) and CD44 (SPP1 receptor) on human BM hematopoietic cells has been demonstrated previously (Ishii et al., 1999; AbuSamra et al., 2017).

These results thus indicated that hematopoietic cells were maintained by different stromal populations through diverse but nevertheless stromal cell-specific pathways. Taking furthermore into account that SPP1-expressing OCs were located endosteally and that CD271 single positive stromal cells including MSSCs were localized in the perivascular and stromal regions, respectively, suggested the possibility that different stromal cells provide specialized niches for hematopoietic cells in different locations. Our data thus confirm and extend previous reports describing that endosteal and perivascular niches exist in human BM (Tormin et al., 2011; Pinho et al., 2013), and conceptually support a model where different hematopoietic cell types are differentially regulated by distinct niche milieus which are composed of specific cytokine-producing stromal cells. Finally, in silico ligand-receptor interaction predictions suggested that endothelial cells and stromal cells showed the potential to communicate with hematopoiesis through different mechanisms, which is certainly an interesting finding that needs to be further addressed in future studies.

In this study, we used human BM aspiration samples in which bone-lining or bone-attaching cells are most likely underrepresented. Our experiments could therefore ideally be complemented by studies on fresh bone marrow biopsy samples from healthy individuals, which however were not available to us, and which might be difficult to acquire because of ethical considerations. In our study, tri-lineage differentiation capacities of stromal clusters were evaluated using standard in-vitro assays, which may not necessarily reflect the bona fide stromal cell potentials. Thus, these data need to be complemented in follow-up studies by in vivo differentiation studies, for example using humanized BM ossicles. The potential key genes that govern the stromal cell fate commitment were predicted using likelihood-based computation and the stroma-hematopoiesis crosstalk mechanisms were inferred based on ligand-receptor interactions. The identified key genes and interactions will need to be validated in future experiments, for example using in vitro engineered human BM models or in vivo humanized BM ossicles (Dupard et al., 2020). While the CellPhoneDB analysis predicted the communication networks between stromal and hematopoietic cells, this approach infers potential interactions using transcriptomics data without considering the spatial proximity of the cells and functional relevance of the interaction. A more comprehensive overview of cellular communication is anticipated when combined with spatial localization analysis and functional assays in future studies. Despite these limitations, we believe that our results provide the basis for a better understanding of the cellular complexity of the human BM microenvironment and put a new light on the current concept that specialized niches exist for distinct types of hematopoietic stem and progenitor cells (Morrison and Scadden, 2014).

The scRNA-seq matrix data generated in this study have been deposited in the GEO database (GSE190965).

1. 1. Li H
   2. Lang S

   (2023) NCBI Gene Expression Omnibus

   ID GSE190965. Transcriptomic profiling of human bone marrow non-hematopoietic cells.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190965

1. 1. AbuSamra DB
   2. Aleisa FA
   3. Al-Amoodi AS
   4. Jalal Ahmed HM
   5. Chin CJ
   6. Abuelela AF
   7. Bergam P
   8. Sougrat R
   9. Merzaban JS

   (2017) Not just a marker: CD34 on human hematopoietic stem/progenitor cells dominates vascular selectin binding along with CD44

   Blood Advances 1:2799–2816.

   https://doi.org/10.1182/bloodadvances.2017004317

   • PubMed
   • Google Scholar
2. 1. Aguilar-Navarro AG
   2. Meza-León B
   3. Gratzinger D
   4. Juárez-Aguilar FG
   5. Chang Q
   6. Ornatsky O
   7. Tsui H
   8. Esquivel-Gómez R
   9. Hernández-Ramírez A
   10. Xie SZ
   11. Dick JE
   12. Flores-Figueroa E

   (2020) Human aging alters the spatial organization between CD34+ hematopoietic cells and adipocytes in bone marrow

   Stem Cell Reports 15:317–325.

   https://doi.org/10.1016/j.stemcr.2020.06.011

   • PubMed
   • Google Scholar
3. 1. Ambrosi TH
   2. Scialdone A
   3. Graja A
   4. Gohlke S
   5. Jank AM
   6. Bocian C
   7. Woelk L
   8. Fan H
   9. Logan DW
   10. Schürmann A
   11. Saraiva LR
   12. Schulz TJ

   (2017) Adipocyte accumulation in the bone marrow during obesity and aging impairs stem cell-based hematopoietic and bone regeneration

   Stem Cell 20:771–784.

   https://doi.org/10.1016/j.stem.2017.02.009

   • PubMed
   • Google Scholar
4. 1. Baccin C
   2. Al-Sabah J
   3. Velten L
   4. Helbling PM
   5. Grünschläger F
   6. Hernández-Malmierca P
   7. Nombela-Arrieta C
   8. Steinmetz LM
   9. Trumpp A
   10. Haas S

   (2020) Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization

   Nature Cell Biology 22:38–48.

   https://doi.org/10.1038/s41556-019-0439-6

   • PubMed
   • Google Scholar
5. 1. Baksh D
   2. Davies JE
   3. Zandstra PW

   (2005) Soluble factor cross-talk between human bone marrow-derived hematopoietic and mesenchymal cells enhances in vitro CFU-F and CFU-O growth and reveals heterogeneity in the mesenchymal progenitor cell compartment

   Blood 106:3012–3019.

   https://doi.org/10.1182/blood-2005-01-0433

   • PubMed
   • Google Scholar
6. 1. Baryawno N
   2. Przybylski D
   3. Kowalczyk MS
   4. Kfoury Y
   5. Severe N
   6. Gustafsson K
   7. Kokkaliaris KD
   8. Mercier F
   9. Tabaka M
   10. Hofree M
   11. Dionne D
   12. Papazian A
   13. Lee D
   14. Ashenberg O
   15. Subramanian A
   16. Vaishnav ED
   17. Rozenblatt-Rosen O
   18. Regev A
   19. Scadden DT

   (2019) A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia

   Cell 177:1915–1932.

   https://doi.org/10.1016/j.cell.2019.04.040

   • PubMed
   • Google Scholar
7. 1. Bergen V
   2. Lange M
   3. Peidli S
   4. Wolf FA
   5. Theis FJ

   (2020) Generalizing RNA velocity to transient cell states through dynamical modeling

   Nature Biotechnology 38:1408–1414.

   https://doi.org/10.1038/s41587-020-0591-3

   • PubMed
   • Google Scholar
8. 1. Boulais PE
   2. Mizoguchi T
   3. Zimmerman S
   4. Nakahara F
   5. Vivié J
   6. Mar JC
   7. van Oudenaarden A
   8. Frenette PS

   (2018) The majority of CD45– ter119– CD31– bone marrow cell fraction is of hematopoietic origin and contains erythroid and lymphoid progenitors

   Immunity 49:627–639.

   https://doi.org/10.1016/j.immuni.2018.08.019

   • Google Scholar
9. 1. Cao Z
   2. Umek RM
   3. McKnight SL

   (1991) Regulated expression of three C/EBP isoforms during adipose conversion of 3T3-L1 cells

   Genes & Development 5:1538–1552.

   https://doi.org/10.1101/gad.5.9.1538

   • PubMed
   • Google Scholar
10. 1. Casanova-Acebes M
    2. Pitaval C
    3. Weiss LA
    4. Nombela-Arrieta C
    5. Chèvre R
    6. A-González N
    7. Kunisaki Y
    8. Zhang D
    9. van Rooijen N
    10. Silberstein LE
    11. Weber C
    12. Nagasawa T
    13. Frenette PS
    14. Castrillo A
    15. Hidalgo A

    (2013) Rhythmic modulation of the hematopoietic niche through neutrophil clearance

    Cell 153:1025–1035.

    https://doi.org/10.1016/j.cell.2013.04.040

    • PubMed
    • Google Scholar
11. 1. Chan CKF
    2. Gulati GS
    3. Sinha R
    4. Tompkins JV
    5. Lopez M
    6. Carter AC
    7. Ransom RC
    8. Reinisch A
    9. Wearda T
    10. Murphy M
    11. Brewer RE
    12. Koepke LS
    13. Marecic O
    14. Manjunath A
    15. Seo EY
    16. Leavitt T
    17. Lu WJ
    18. Nguyen A
    19. Conley SD
    20. Salhotra A
    21. Ambrosi TH
    22. Borrelli MR
    23. Siebel T
    24. Chan K
    25. Schallmoser K
    26. Seita J
    27. Sahoo D
    28. Goodnough H
    29. Bishop J
    30. Gardner M
    31. Majeti R
    32. Wan DC
    33. Goodman S
    34. Weissman IL
    35. Chang HY
    36. Longaker MT

    (2018) Identification of the human skeletal stem cell

    Cell 175:43–56.

    https://doi.org/10.1016/j.cell.2018.07.029

    • PubMed
    • Google Scholar
12. 1. Churchman SM
    2. Ponchel F
    3. Boxall SA
    4. Cuthbert R
    5. Kouroupis D
    6. Roshdy T
    7. Giannoudis PV
    8. Emery P
    9. McGonagle D
    10. Jones EA

    (2012) Transcriptional profile of native CD271+ multipotential stromal cells: evidence for multiple fates, with prominent osteogenic and Wnt pathway signaling activity

    Arthritis and Rheumatism 64:2632–2643.

    https://doi.org/10.1002/art.34434

    • PubMed
    • Google Scholar
13. 1. de Jong MME
    2. Kellermayer Z
    3. Papazian N
    4. Tahri S
    5. Hofste Op Bruinink D
    6. Hoogenboezem R
    7. Sanders MA
    8. van de Woestijne PC
    9. Bos PK
    10. Khandanpour C
    11. Vermeulen J
    12. Moreau P
    13. van Duin M
    14. Broijl A
    15. Sonneveld P
    16. Cupedo T

    (2021) The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape

    Nature Immunology 22:769–780.

    https://doi.org/10.1038/s41590-021-00931-3

    • PubMed
    • Google Scholar
14. 1. Delorme B
    2. Ringe J
    3. Gallay N
    4. Le Vern Y
    5. Kerboeuf D
    6. Jorgensen C
    7. Rosset P
    8. Sensebé L
    9. Layrolle P
    10. Häupl T
    11. Charbord P

    (2008) Specific plasma membrane protein phenotype of culture-amplified and native human bone marrow mesenchymal stem cells

    Blood 111:2631–2635.

    https://doi.org/10.1182/blood-2007-07-099622

    • PubMed
    • Google Scholar
15. 1. Ding L
    2. Saunders TL
    3. Enikolopov G
    4. Morrison SJ

    (2012) Endothelial and perivascular cells maintain haematopoietic stem cells

    Nature 481:457–462.

    https://doi.org/10.1038/nature10783

    • PubMed
    • Google Scholar
16. 1. Dupard SJ
    2. Grigoryan A
    3. Farhat S
    4. Coutu DL
    5. Bourgine PE

    (2020) Development of humanized ossicles: bridging the hematopoietic gap

    Trends in Molecular Medicine 26:552–569.

    https://doi.org/10.1016/j.molmed.2020.01.016

    • PubMed
    • Google Scholar
17. 1. Efremova M
    2. Vento-Tormo M
    3. Teichmann SA
    4. Vento-Tormo R

    (2020) CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes

    Nature Protocols 15:1484–1506.

    https://doi.org/10.1038/s41596-020-0292-x

    • PubMed
    • Google Scholar
18. 1. Gao S
    2. Shi Q
    3. Zhang Y
    4. Liang G
    5. Kang Z
    6. Huang B
    7. Ma D
    8. Wang L
    9. Jiao J
    10. Fang X
    11. Xu CR
    12. Liu L
    13. Xu X
    14. Göttgens B
    15. Li C
    16. Liu F

    (2022) Identification of hsc/mpp expansion units in fetal liver by single-cell spatiotemporal transcriptomics

    Cell Research 32:38–53.

    https://doi.org/10.1038/s41422-021-00540-7

    • PubMed
    • Google Scholar
19. 1. Gronthos S
    2. Fitter S
    3. Diamond P
    4. Simmons PJ
    5. Itescu S
    6. Zannettino ACW

    (2007) A novel monoclonal antibody (STRO-3) identifies an isoform of tissue nonspecific alkaline phosphatase expressed by multipotent bone marrow stromal stem cells

    Stem Cells and Development 16:953–963.

    https://doi.org/10.1089/scd.2007.0069

    • PubMed
    • Google Scholar
20. 1. Headland SE
    2. Jones HR
    3. Norling LV
    4. Kim A
    5. Souza PR
    6. Corsiero E
    7. Gil CD
    8. Nerviani A
    9. Dell’Accio F
    10. Pitzalis C
    11. Oliani SM
    12. Jan LY
    13. Perretti M

    (2015) Neutrophil-Derived microvesicles enter cartilage and protect the joint in inflammatory arthritis

    Science Translational Medicine 7:315ra190.

    https://doi.org/10.1126/scitranslmed.aac5608

    • PubMed
    • Google Scholar
21. 1. Ishii T
    2. Nishihara M
    3. Ma F
    4. Ebihara Y
    5. Tsuji K
    6. Asano S
    7. Nakahata T
    8. Maekawa T

    (1999) Expression of stromal cell-derived factor-1/pre-B cell growth-stimulating factor receptor, CXC chemokine receptor 4, on CD34+ human bone marrow cells is a phenotypic alteration for committed lymphoid progenitors

    Journal of Immunology 163:3612–3620.

    https://doi.org/10.4049/jimmunol.163.7.3612

    • PubMed
    • Google Scholar
22. 1. Jones EA
    2. Kinsey SE
    3. English A
    4. Jones RA
    5. Straszynski L
    6. Meredith DM
    7. Markham AF
    8. Jack A
    9. Emery P
    10. McGonagle D

    (2002) Isolation and characterization of bone marrow multipotential mesenchymal progenitor cells

    Arthritis and Rheumatism 46:3349–3360.

    https://doi.org/10.1002/art.10696

    • PubMed
    • Google Scholar
23. 1. Kleven MD
    2. Jue S
    3. Enns CA

    (2018) Transferrin receptors TfR1 and TfR2 bind transferrin through differing mechanisms

    Biochemistry 57:1552–1559.

    https://doi.org/10.1021/acs.biochem.8b00006

    • PubMed
    • Google Scholar
24. 1. Li H
    2. Ghazanfari R
    3. Zacharaki D
    4. Ditzel N
    5. Isern J
    6. Ekblom M
    7. Méndez-Ferrer S
    8. Kassem M
    9. Scheding S

    (2014) Low/negative expression of PDGFR-α identifies the candidate primary mesenchymal stromal cells in adult human bone marrow

    Stem Cell Reports 3:965–974.

    https://doi.org/10.1016/j.stemcr.2014.09.018

    • PubMed
    • Google Scholar
25. 1. Li H.
    2. Ghazanfari R
    3. Zacharaki D
    4. Lim HC
    5. Scheding S

    (2016) Isolation and characterization of primary bone marrow mesenchymal stromal cells

    Annals of the New York Academy of Sciences 1370:109–118.

    https://doi.org/10.1111/nyas.13102

    • PubMed
    • Google Scholar
26. 1. Li H
    2. Lim HC
    3. Zacharaki D
    4. Xian X
    5. Kenswil KJG
    6. Bräunig S
    7. Raaijmakers M
    8. Woods NB
    9. Hansson J
    10. Scheding S

    (2020) Early growth response 1 regulates hematopoietic support and proliferation in human primary bone marrow stromal cells

    Haematologica 105:1206–1215.

    https://doi.org/10.3324/haematol.2019.216648

    • PubMed
    • Google Scholar
27. 1. Maryanovich M
    2. Zahalka AH
    3. Pierce H
    4. Pinho S
    5. Nakahara F
    6. Asada N
    7. Wei Q
    8. Wang X
    9. Ciero P
    10. Xu J
    11. Leftin A
    12. Frenette PS

    (2018) Adrenergic nerve degeneration in bone marrow drives aging of the hematopoietic stem cell niche

    Nature Medicine 24:782–791.

    https://doi.org/10.1038/s41591-018-0030-x

    • PubMed
    • Google Scholar
28. 1. Mende N
    2. Jolly A
    3. Percin GI
    4. Günther M
    5. Rostovskaya M
    6. Krishnan SM
    7. Oostendorp RAJ
    8. Dahl A
    9. Anastassiadis K
    10. Höfer T
    11. Waskow C

    (2019) Prospective isolation of nonhematopoietic cells of the niche and their differential molecular interactions with HSCs

    Blood 134:1214–1226.

    https://doi.org/10.1182/blood.2019000176

    • PubMed
    • Google Scholar
29. 1. Méndez-Ferrer S
    2. Lucas D
    3. Battista M
    4. Frenette PS

    (2008) Haematopoietic stem cell release is regulated by circadian oscillations

    Nature 452:442–447.

    https://doi.org/10.1038/nature06685

    • PubMed
    • Google Scholar
30. 1. Morrison SJ
    2. Scadden DT

    (2014) The bone marrow niche for haematopoietic stem cells

    Nature 505:327–334.

    https://doi.org/10.1038/nature12984

    • PubMed
    • Google Scholar
31. 1. Nakamura M
    2. Sone S
    3. Takahashi I
    4. Mizoguchi I
    5. Echigo S
    6. Sasano Y

    (2005) Expression of versican and ADAMTS1, 4, and 5 during bone development in the rat mandible and hind limb

    The Journal of Histochemistry and Cytochemistry 53:1553–1562.

    https://doi.org/10.1369/jhc.5A6669.2005

    • PubMed
    • Google Scholar
32. 1. Pinho S
    2. Lacombe J
    3. Hanoun M
    4. Mizoguchi T
    5. Bruns I
    6. Kunisaki Y
    7. Frenette PS

    (2013) Pdgfrα and CD51 mark human nestin+ sphere-forming mesenchymal stem cells capable of hematopoietic progenitor cell expansion

    The Journal of Experimental Medicine 210:1351–1367.

    https://doi.org/10.1084/jem.20122252

    • PubMed
    • Google Scholar
33. 1. Pinho S
    2. Frenette PS

    (2019) Haematopoietic stem cell activity and interactions with the niche

    Nature Reviews. Molecular Cell Biology 20:303–320.

    https://doi.org/10.1038/s41580-019-0103-9

    • PubMed
    • Google Scholar
34. 1. Pittenger MF
    2. Mackay AM
    3. Beck SC
    4. Jaiswal RK
    5. Douglas R
    6. Mosca JD
    7. Moorman MA
    8. Simonetti DW
    9. Craig S
    10. Marshak DR

    (1999) Multilineage potential of adult human mesenchymal stem cells

    Science 284:143–147.

    https://doi.org/10.1126/science.284.5411.143

    • PubMed
    • Google Scholar
35. 1. Quirici N
    2. Soligo D
    3. Bossolasco P
    4. Servida F
    5. Lumini C
    6. Deliliers GL

    (2002) Isolation of bone marrow mesenchymal stem cells by anti-nerve growth factor receptor antibodies

    Experimental Hematology 30:783–791.

    https://doi.org/10.1016/s0301-472x(02)00812-3

    • PubMed
    • Google Scholar
36. 1. Sacchetti B
    2. Funari A
    3. Michienzi S
    4. Di Cesare S
    5. Piersanti S
    6. Saggio I
    7. Tagliafico E
    8. Ferrari S
    9. Robey PG
    10. Riminucci M
    11. Bianco P

    (2007) Self-Renewing osteoprogenitors in bone marrow sinusoids can organize a hematopoietic microenvironment

    Cell 131:324–336.

    https://doi.org/10.1016/j.cell.2007.08.025

    • PubMed
    • Google Scholar
37. 1. Singh L
    2. Brennan TA
    3. Russell E
    4. Kim J-H
    5. Chen Q
    6. Brad Johnson F
    7. Pignolo RJ

    (2016) Aging alters bone-fat reciprocity by shifting in vivo mesenchymal precursor cell fate towards an adipogenic lineage

    Bone 85:29–36.

    https://doi.org/10.1016/j.bone.2016.01.014

    • PubMed
    • Google Scholar
38. 1. Słomnicki LP
    2. Leśniak W

    (2010) S100A6 (calcyclin) deficiency induces senescence-like changes in cell cycle, morphology and functional characteristics of mouse NIH 3T3 fibroblasts

    Journal of Cellular Biochemistry 109:576–584.

    https://doi.org/10.1002/jcb.22434

    • PubMed
    • Google Scholar
39. Preprint

    1. Sommarin MNE
    2. Dhapola P
    3. Safi F
    4. Warfvinge R
    5. Ulfsson LG
    6. Erlandsson E
    7. Konturek-Ciesla A
    8. Thakur RK
    9. Böiers C
    10. Bryder D
    11. Karlsson G

    (2021) Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy

    bioRxiv.

    https://doi.org/10.1101/2021.04.01.437998

    • Google Scholar
40. Software

    1. Stefan L

    (2022) MSC_BM_scripts, version swh:1:rev:22c09a6f37f4cd46f977b3a97b9e8139776030bf

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:6d9e5579002aed7c2d672a7d012582f58bd46c85;origin=https://github.com/Hongzhe2022/MSC_BM_scripts;visit=swh:1:snp:fa5eb0d5e43a9663a2168e9ab37f127b0c89930d;anchor=swh:1:rev:22c09a6f37f4cd46f977b3a97b9e8139776030bf
41. 1. Svensson V
    2. Pachter L

    (2018) Rna velocity: molecular kinetics from single-cell RNA-seq

    Molecular Cell 72:7–9.

    https://doi.org/10.1016/j.molcel.2018.09.026

    • PubMed
    • Google Scholar
42. 1. Tikhonova AN
    2. Dolgalev I
    3. Hu H
    4. Sivaraj KK
    5. Hoxha E
    6. Cuesta-Domínguez Á
    7. Pinho S
    8. Akhmetzyanova I
    9. Gao J
    10. Witkowski M
    11. Guillamot M
    12. Gutkin MC
    13. Zhang Y
    14. Marier C
    15. Diefenbach C
    16. Kousteni S
    17. Heguy A
    18. Zhong H
    19. Fooksman DR
    20. Butler JM
    21. Economides A
    22. Frenette PS
    23. Adams RH
    24. Satija R
    25. Tsirigos A
    26. Aifantis I

    (2019) The bone marrow microenvironment at single-cell resolution

    Nature 569:222–228.

    https://doi.org/10.1038/s41586-019-1104-8

    • PubMed
    • Google Scholar
43. 1. Tormin A
    2. Li O
    3. Brune JC
    4. Walsh S
    5. Schütz B
    6. Ehinger M
    7. Ditzel N
    8. Kassem M
    9. Scheding S

    (2011) Cd146 expression on primary nonhematopoietic bone marrow stem cells is correlated with in situ localization

    Blood 117:5067–5077.

    https://doi.org/10.1182/blood-2010-08-304287

    • PubMed
    • Google Scholar
44. 1. Triana S
    2. Vonficht D
    3. Jopp-Saile L
    4. Raffel S
    5. Lutz R
    6. Leonce D
    7. Antes M
    8. Hernández-Malmierca P
    9. Ordoñez-Rueda D
    10. Ramasz B
    11. Boch T
    12. Jann J-C
    13. Nowak D
    14. Hofmann W-K
    15. Müller-Tidow C
    16. Hübschmann D
    17. Alexandrov T
    18. Benes V
    19. Trumpp A
    20. Paulsen M
    21. Velten L
    22. Haas S

    (2021) Single-Cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

    Nature Immunology 22:1577–1589.

    https://doi.org/10.1038/s41590-021-01059-0

    • PubMed
    • Google Scholar
45. 1. Urbieta M
    2. Barao I
    3. Jones M
    4. Jurecic R
    5. Panoskaltsis-Mortari A
    6. Blazar BR
    7. Murphy WJ
    8. Levy RB

    (2010) Hematopoietic progenitor cell regulation by CD4+CD25+ T cells

    Blood 115:4934–4943.

    https://doi.org/10.1182/blood-2009-04-218826

    • PubMed
    • Google Scholar
46. 1. Wang Z
    2. Li X
    3. Yang J
    4. Gong Y
    5. Zhang H
    6. Qiu X
    7. Liu Y
    8. Zhou C
    9. Chen Y
    10. Greenbaum J
    11. Cheng L
    12. Hu Y
    13. Xie J
    14. Yang X
    15. Li Y
    16. Schiller MR
    17. Chen Y
    18. Tan L
    19. Tang SY
    20. Shen H
    21. Xiao HM
    22. Deng HW

    (2021) Single-Cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells

    International Journal of Biological Sciences 17:4192–4206.

    https://doi.org/10.7150/ijbs.61950

    • PubMed
    • Google Scholar
47. 1. Wolock SL
    2. Krishnan I
    3. Tenen DE
    4. Matkins V
    5. Camacho V
    6. Patel S
    7. Agarwal P
    8. Bhatia R
    9. Tenen DG
    10. Klein AM
    11. Welner RS

    (2019) Mapping distinct bone marrow niche populations and their differentiation paths

    Cell Reports 28:302–311.

    https://doi.org/10.1016/j.celrep.2019.06.031

    • PubMed
    • Google Scholar
48. 1. Yamazaki S
    2. Ema H
    3. Karlsson G
    4. Yamaguchi T
    5. Miyoshi H
    6. Shioda S
    7. Taketo MM
    8. Karlsson S
    9. Iwama A
    10. Nakauchi H

    (2011) Nonmyelinating Schwann cells maintain hematopoietic stem cell hibernation in the bone marrow niche

    Cell 147:1146–1158.

    https://doi.org/10.1016/j.cell.2011.09.053

    • PubMed
    • Google Scholar
49. 1. Zhao M
    2. Perry JM
    3. Marshall H
    4. Venkatraman A
    5. Qian P
    6. He XC
    7. Ahamed J
    8. Li L

    (2014) Megakaryocytes maintain homeostatic quiescence and promote post-injury regeneration of hematopoietic stem cells

    Nature Medicine 20:1321–1326.

    https://doi.org/10.1038/nm.3706

    • PubMed
    • Google Scholar
50. 1. Zhou BO
    2. Yue R
    3. Murphy MM
    4. Peyer JG
    5. Morrison SJ

    (2014) Leptin-receptor-expressing mesenchymal stromal cells represent the main source of bone formed by adult bone marrow

    Stem Cell 15:154–168.

    https://doi.org/10.1016/j.stem.2014.06.008

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Single-cell RNA sequencing identifies phenotypically, functionally, and anatomically distinct stromal niche populations in human bone marrow" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mone Zaidi as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Dirk Strunk (Reviewer #1); Robert A. J. Oostendorp (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Collectively, the reviewers agree that your study is an important scRNAseq resource and appreciate the potential interest of your predictions about different cell populations, their lineage relationships, and how they might interact with hematopoietic cells. While functional validations are outside the scope of this inherently descriptive large dataset and would not allow timely publication, the conclusions regarding cell populations and lineage relationships should be toned down and presented as possible suggestions, pending future validations.

Essential revisions:

1) Please provide a glossary containing the abbreviations used to facilitate accessibility.

2) Tone down conclusive statements about predictions, to avoid over-interpretation of the data.

3) Re-analyze or present some of the data to facilitate viewing of the relevant cell-cell interactions or the cell populations identified using candidate markers (specific comments or guidelines are provided by the reviewers in their review).

4) Clarify the conflicting results of adipo/osteogenic commitment inferred from transcriptomic and in vitro differentiation studies.

5) Incorporate textual revisions explained by the reviewers in their detailed comments.

Reviewer #1 (Recommendations for the authors):

– Please report the frequency/numbers of sorted CD45low/-CDa-/CD271+ cells and their enriched CXCL subfraction if available.

Reviewer #2 (Recommendations for the authors):

– A glossary with all the different abbreviations would be helpful, as some of them are used prior to their definition/description in the text (for instance MSSC).

– Several of the statements in this manuscript are about predictions, speculations, or hypotheses, which appear to be over-interpretions of the data. Thus, these statements should be toned down, unless the authors can provide clear experimental evidence confirming these statements.

– One such statement is in the abstract and mentions specific BM regions "using" CXCL12 or SPP1. This should reformulate, as hard evidence to support this statement is not provided.

Essential revisions:

1) Please provide a glossary containing the abbreviations used to facilitate accessibility.

We thank the reviewer for the suggestion and a list of abbreviations is now provided on page 2.

2) Tone down conclusive statements about predictions, to avoid over-interpretation of the data.

Thank you for this comment. Being aware that we have not provided final experimental proof that validates the potential key drivers of stromal cell fate and the predicted ligand-receptor interactions and functional roles in the current study, we have toned down the corresponding statements as suggested by the reviewer (page 3, lines 11-12; page 18, lines 5-7; page 26, lines 16-21; page 29, lines 18-20; page 31, lines 19-23; page 32, lines 2-5).

3) Re-analyze or present some of the data to facilitate viewing of the relevant cell-cell interactions or the cell populations identified using candidate markers (specific comments or guidelines are provided by the reviewers in their review).

For the cell-cell interaction analysis, reviewer 2 suggested to re-analyze the data to show the specificity of the interactions. However, as shown in our original Figure 5B-C and Figure 5—figure supplement 1B, interactions between MSSC/OC with hematopoietic clusters and the CXCL12- and SPP1-mediated stromal cell type-specific interactions were already presented. Therefore and as outlined in detail in the response to the reviewer, we feel that a re-analysis of the data at this point would not further facilitate viewing of the relevant cellcell interactions.

Reviewer 2 also suggested in the detailed comment that we should describe the possible interactions between non-hematopoietic cells. These interactions were already shown in our original Figure 7A and Figure 5—figure supplement 3B. For the stromal cells, both inter- and intra-stromal interactions were identified to be operative between different stromal subsets as well as within the same stromal cell population (Figure 5—figure supplement 3B). We also analyzed and presented the interaction pattern between endothelial cells and hematopoietic cells (Figure 7A) and a couple of endothelial-specific interactions were detected (SELLSELPLG and ICAM3-aLb2 complex). Therefore, we do not think that additional analysis regarding these interactions is needed.

With regard to the scRNAseq analysis, reviewer 2 suggested in the detailed comments that we should highlight the expression of CD45, CD235a, and CD271 in different analyses and restrict the analyses accordingly. According to this comment, we have modified the figures which are presented in the new Figure R5. However, because the inclusion of these markers in the figures did not provide substantial novel information and because there is a risk of excluding important stromal cells from the analysis, we chose to not make changes to the original figure (Figure 1D, 2B-F and 4A).

Please see also our response to the reviewers’ comments for a detailed discussion of these points.

4) Clarify the conflicting results of adipo/osteogenic commitment inferred from transcriptomic and in vitro differentiation studies.

We thank the editor and the reviewer for this comment. Cluster 5 was initially annotated as “adipo-primed” cluster based on the higher expression of adipogenic differentiation markers as well as a group of stress-related transcription factors (FOS, FOSB, JUNB, EGR1) (Figure 2B-C, Figure 2—figure supplement 1C), some of which had been reported to mark bone marrow adipogenic progenitors¹. Although at considerably lower levels compared to adipogenic genes, osteogenic genes were also expressed in cluster 5 cells (Figure 2B and D), which indicated a potentially broader differentiation potential of this cluster. As correctly noticed by the reviewers, the initial annotation of this cluster as “adipo-primed progenitors” unfortunately did not reflect this fact correctly. Therefore we have renamed these cells as ‘highly adipocytic gene-expressing progenitors (HAGEPs)” in the revised version of the manuscript. This new name does also more correctly reflect the results from the differentiation studies.

5) Incorporate textual revisions explained by the reviewers in their detailed comments.

We have changed the manuscript according to the reviewers’ comments. Please see our detailed responses below.

Reviewer #1 (Recommendations for the authors):

– Please report the frequency/numbers of sorted CD45low/-CDa-/CD271+ cells and their enriched CXCL subfraction if available.

The cell numbers and percentages of sorted CD45^low/-CD235a^- and CD45^low/-CD235a^-CD271⁺ cells have been listed in Supplementary File 3 (sheet 1). The numbers and frequency of CXCL12-expressing cells in sorted CD45^low/-CD235a^- and CD45^low/-CD235a^-CD271⁺ cells have now bwwn listed in Supplementary File 3 (sheet 4).

Reviewer #2 (Recommendations for the authors):

– A glossary with all the different abbreviations would be helpful, as some of them are used prior to their definition/description in the text (for instance MSSC).

We thank the reviewer for the suggestion. A list of abbreviations is now provided on page 2 of the manuscript.

– Several of the statements in this manuscript are about predictions, speculations, or hypotheses, which appear to be over-interpretions of the data. Thus, these statements should be toned down, unless the authors can provide clear experimental evidence confirming these statements.

Thank you for this comment. Being aware that we have not provided experimental proof to validate the role of potential key drivers of stromal cell fate, the predicted ligand receptor interactions and functional roles in the current study, we have toned down the corresponding statements as suggested by the reviewer (page 3, lines 11-12; page 18, lines 57; page 26, lines 16-21; page 29, lines 18-20; page 31, lines 19-23; page 32, lines 2-5).

– One such statement is in the abstract and mentions specific BM regions "using" CXCL12 or SPP1. This should reformulate, as hard evidence to support this statement is not provided.

We have changed the abstract according to the reviewer’s comment (page 3, lines 12-15).

References

Ambrosi TH, Scialdone A, Graja A, et al. Adipocyte Accumulation in the Bone Marrow during Obesity and Aging Impairs Stem Cell-Based Hematopoietic and Bone Regeneration. Cell Stem Cell. 2017;20(6):771-784 e776.

Sutherland DR, Anderson L, Keeney M, Nayar R, Chin-Yee I. The ISHAGE guidelines for CD34+ cell determination by flow cytometry. International Society of Hematotherapy and Graft Engineering. J Hematother. 1996;5(3):213-226.

Boulais PE, Mizoguchi T, Zimmerman S, et al. The Majority of CD45(-) Ter119(-) CD31(-) Bone Marrow Cell Fraction Is of Hematopoietic Origin and Contains Erythroid and Lymphoid Progenitors. Immunity. 2018;49(4):627-639 e626.

Baryawno N, Przybylski D, Kowalczyk MS, et al. A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia. Cell. 2019;177(7):1915-1932 e1916.

Sotoodehnejadnematalahi F, Staples KJ, Chrysanthou E, Pearson H, Ziegler-Heitbrock L, Burke B. Mechanisms of Hypoxic Up-Regulation of Versican Gene Expression in Macrophages. PLoS One. 2015;10(6):e0125799.

Wang J, Dodd C, Shankowsky HA, Scott PG, Tredget EE, Wound Healing Research G. Deep dermal fibroblasts contribute to hypertrophic scarring. Lab Invest. 2008;88(12):1278-1290. 7. Zhou BO, Yue R, Murphy MM, Peyer JG, Morrison SJ. Leptin-receptor-expressing mesenchymal stromal cells represent the main source of bone formed by adult bone marrow. Cell Stem Cell. 2014;15(2):154-168.

Li H, Ghazanfari R, Zacharaki D, et al. Low/negative expression of PDGFR-α identifies the candidate primary mesenchymal stromal cells in adult human bone marrow. Stem Cell Reports. 2014;3(6):965-974.

Kathiriya JJ, Wang C, Zhou M, et al. Human alveolar type 2 epithelium transdifferentiates into metaplastic KRT5(+) basal cells. Nat Cell Biol. 2022;24(1):10-23.

Wangen JR, Eidenschink Brodersen L, Stolk TT, Wells DA, Loken MR. Assessment of normal erythropoiesis by flow cytometry: important considerations for specimen preparation. Int J Lab Hematol. 2014;36(2):184-196.

Tormin A, Li O, Brune JC, et al. CD146 expression on primary nonhematopoietic bone marrow stem cells is correlated with in situ localization. Blood. 2011;117(19):5067-5077.

Chan CKF, Gulati GS, Sinha R, et al. Identification of the Human Skeletal Stem Cell. Cell. 2018;175(1):43-56 e21.

Pinho S, Lacombe J, Hanoun M, et al. PDGFRalpha and CD51 mark human nestin+ sphereforming mesenchymal stem cells capable of hematopoietic progenitor cell expansion. J Exp Med. 2013;210(7):1351-1367.

Lucas S, Tencerova M, von der Weid B, et al. Guidelines for Biobanking of Bone Marrow Adipose Tissue and Related Cell Types: Report of the Biobanking Working Group of the International Bone Marrow Adiposity Society. Front Endocrinol (Lausanne). 2021;12:744527.

Merrick D, Sakers A, Irgebay Z, et al. Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science. 2019;364(6438).

Wolock SL, Krishnan I, Tenen DE, et al. Mapping Distinct Bone Marrow Niche Populations and Their Differentiation Paths. Cell Rep. 2019;28(2):302-311 e305.

de Jong MME, Kellermayer Z, Papazian N, et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat Immunol. 2021;22(6):769-780.

Leimkuhler NB, Gleitz HFE, Ronghui L, et al. Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis. Cell Stem Cell. 2021;28(4):637-652 e638.

Author details

1. Hongzhe Li

   Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   hongzhe.li@med.lu.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7788-878X

2. Sandro Bräunig

   Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4418-0854

3. Parashar Dhapolar

   Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden

   Contribution

   Data curation, Validation, Investigation

   Competing interests

   No competing interests declared

4. Göran Karlsson

   Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden

   Contribution

   Supervision

   Competing interests

   No competing interests declared

5. Stefan Lang

   Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

6. Stefan Scheding

   1. Division of Molecular Hematology and Stem Cell Center, Lund University, Lund, Sweden
   2. Department of Hematology, Skåne University Hospital, Lund, Sweden

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   stefan.scheding@med.lu.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8005-9568

• 2,212

  Page views

• 438

  Downloads

• 3

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.