Single-cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells.

Bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stromal cells that have a critical role in the maintenance of skeletal tissues such as bone, cartilage, and the fat in bone marrow. In addition to providing microenvironmental support for hematopoietic processes, BM-MSCs can differentiate into various mesodermal lineages including osteoblast/osteocyte, chondrocyte, and adipocyte that are crucial for bone metabolism. While BM-MSCs have high cell-to-cell heterogeneity in gene expression, the cell subtypes that contribute to this heterogeneity in vivo in humans have not been characterized. To investigate the transcriptional diversity of BM-MSCs, we applied single-cell RNA sequencing (scRNA-seq) on freshly isolated CD271+ BM-derived mononuclear cells (BM-MNCs) from two human subjects. We successfully identified LEPRhiCD45low BM-MSCs within the CD271+ BM-MNC population, and further codified the BM-MSCs into distinct subpopulations corresponding to the osteogenic, chondrogenic, and adipogenic differentiation trajectories, as well as terminal-stage quiescent cells. Biological functional annotations of the transcriptomes suggest that osteoblast precursors induce angiogenesis coupled with osteogenesis, and chondrocyte precursors have the potential to differentiate into myocytes. We also discovered transcripts for several clusters of differentiation (CD) markers that were either highly expressed (e.g., CD167b, CD91, CD130 and CD118) or absent (e.g., CD74, CD217, CD148 and CD68) in BM-MSCs, representing potential novel markers for human BM-MSC purification. This study is the first systematic in vivo dissection of human BM-MSCs cell subtypes at the single-cell resolution, revealing an insight into the extent of their cellular heterogeneity and roles in maintaining bone homeostasis.

Heterogeneous populations from in vitro cultures of antigen presenting cells in pigs.

Dendritic cells (DCs) are the most potent antigen presenting cells (APCs). Because of the difficulty in obtaining these cells directly from tissues, different sources of DCs are frequently used for in vitro experimentation and many of their biological and functional characteristics were studied using these systems. Until recently, it was assumed that specific culture conditions polarized the differentiation of either DCs or macrophages (Macs); however, it was shown that some DC culture systems in other species generate heterogeneous cell populations that can be identified according to their CD11c and MHC class II (MHC-II) expression. Following this approach, porcine DCs were directly isolated from peripheral blood or differentiated in vitro by culturing bone marrow (BM) progenitor cells or blood monocytes treated with growth factors. Mostly homogeneous monocyte-derived DCs (MoDCs) were obtained with similar phenotype and phagocytic characteristics to that of blood DCs. On the contrary, BM-derived DC (BMDC) cultures generated two distinct heterogeneous populations identified as MHC-II+ and MHC-II++ cells. BMDCs MHC-II+ had similar phenotypic and phagocytic characteristics to those of MoDCs and blood DCs. However, BMDCs MHC-II++ population expressed a higher amount of surface markers and transcribed genes associated with Macs-lineage exhibiting a higher phagocytic capacity than all the other cells. Noteworthy, every cell system expressed different genetic signatures. These results will help interpreting and re-interpreting data obtained using in vitro systems.

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing.

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning-the branch of machine learning that concerns passing information from one domain to another-can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Protocol for the Maintenance of Quiescent Murine Hematopoietic Stem Cells.

Mammalian hematopoietic stem cells (HSCs) maintain life-long hematopoiesis in the bone marrow. HSCs remain quiescent in vivo, unlike more differentiated progenitors, and enter the cell cycle rapidly after bone marrow injury or in vitro culture. We have recently demonstrated the ability to maintain HSC quiescence in vitro by mimicking the bone marrow microenvironment. Here, we provide a detailed protocol for keeping functional HSCs in the quiescent state in vitro. For complete details on the use and execution of this protocol, please refer to Kobayashi et al. (2019).

Dense carbon-nanotube coating scaffolds stimulate osteogenic differentiation of mesenchymal stem cells.

Carbon nanotubes (CNTs) have desirable mechanical properties for use as biomaterials in orthopedic and dental area such as bone- and tooth- substitutes. Here, we demonstrate that a glass surface densely coated with single-walled carbon nanotubes (SWNTs) stimulate the osteogenic differentiation of rat bone marrow mesenchymal stem cells (MSCs). MSCs incubated on SWNT- and multi-walled carbon nanotube (MWNT)-coated glass showed high activities of alkaline phosphatase that are markers for early stage osteogenic differentiation. Expression of Bmp2, Runx2, and Alpl of MSCs showed high level in the early stage for MSC incubation on SWNT- and MWNT-coated surfaces, but only the cells on the SWNT-coated glass showed high expression levels of Bglap (Osteocalcin). The cells on the SWNT-coated glass also contained the most calcium, and their calcium deposits had long needle-shaped crystals. SWNT coating at high density could be part of a new scaffold for bone regeneration.

Multiparameter flow cytometry applications in the diagnosis of mixed phenotype acute leukemia.

Mixed phenotype acute leukemias (MPALs) represent a rare subgroup of acute leukemias with a poor prognosis. Proper diagnosis and classification of MPAL is extremely important for patients' outcome. Morphology and flow cytometry recognize two types of MPAL: the "bilineal" MPAL with the coexistence of two blast populations of different lineage and truly "biphenotypic" MPAL coexpressing markers of more than one lineage in a homogenous blast population, respectively. The WHO 2008 classification further delineated three categories: associated with t(9;22)/BCR-ABL1 fusion gene, associated with KMT2A (mixed lineage leukemia) rearrangements, and nonotherwise specified. These categories remained unchanged in the WHO2016 update. Molecular studies have further underlined the heterogeneity of MPAL. In this review, rules for the correct assignment of acute leukemia to the MPAL category are discussed, including both flow cytometry and immunohistochemistry on bone marrow or other tissues biopsies. Comparison of the immunophenotypic classification proposals is provided outlining the explorations mandatory for definitive diagnosis. An extensive review of published data summarizes the reported cytogenetic and molecular anomalies. New developments in the understanding of the early stages of hematopoiesis provide clues to the possible etiopathology of these diseases. Finally, current treatment recommendations are summarized and referenced for clinical use, pointing out that allogeneic hematopoietic stem cell transplantation at an early stage should be considered (at least in adult patients). © 2019 International Clinical Cytometry Society.

Predicting Cell Populations in Single Cell Mass Cytometry Data.

Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.

In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu's method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods.

The majority of cells in so-called "mesenchymal stem cell" population are neither stem cells nor progenitors.

OBJECTIVES: The first-passage adherent human bone marrow fibroblast-like cell population corresponds, in terms of phenotype and three-lineage differentiation capacity (assayed in bulk culture), to commonly termed "mesenchymal stem cells". Here we determine the proportion of high proliferative capacity multipotent cells present in this population in order to estimate the proportion of cells that can or cannot be considered as stem and progenitor cells. MATERIAL AND METHODS: The single-cell cultures were established starting from human bone marrow-derived first-passage fibroblast-like cells and the proliferating clones were either transferred to secondary cultures to evaluate their further clonogenicity, or split into three wells to assess differentiation into each of the three different lineages. RESULTS: The analysis of 197 single-cell cultures from three different bone marrow donors shows that only∼40% of so-called "mesenchymal stem cells" exhibit multipotency and are capable of sustained clonogenicity in secondary cultures. CONCLUSION: Even in the first ex vivo passage under favorable conditions the majority (∼60%) of so-called "mesenchymal stem cells" are not multipotent and thus do not represent a stem cell entity.

Naïve/memory T-cell phenotypes in leukemic cutaneous T-cell lymphoma: Putative cell of origin overlaps disease classification.

BACKGROUND: Mycosis fungoides (MF) and Sézary Syndrome (SS) are clinically distinct cutaneous T-cell lymphomas with strikingly similar morphologic and phenotypic features. Prior studies have suggested phenotypic differences based on markers of antigen experience, suggesting a different cell of origin. METHODS: Seventy-nine involved peripheral blood or bone marrow samples from 33 patients with SS and 19 patients with MF were studied by 10-color flow cytometry, including CD62L, CD45RA, CCR4, and PD-1. Gated tumor events were classified as naïve (TN ), central memory (TCM ), effector memory (TEM ), or effector memory with reacquired CD45RA (TEMRA ); based on CD62L+ /CD45RA+ , CD62L+ /CD45RA- , CD62L- /CD45RA- , or CD62L- /CD45RA+ phenotype, respectively. Sequential specimens were compared to assess for phenotypic stability. RESULTS: The naïve/memory phenotype of the neoplastic T-cells was markedly heterogeneous, with a dominant TN , TCM , TEM , or TEMRA subset on 11 (14%), 32 (41%), 30 (38%), and 6 (8%) cases, respectively. There was no correlation between the diagnosis of MF or SS and putative cell of origin (P = 0.4). Overexpression of CCR4 and PD1 was observed in most cases, with higher intensity in SS compared to MF. The naïve/memory phenotype remained the same for 10 patients up to 273 days after the initial analysis; while on six patients, the naïve/memory phenotype was different from the original phenotype. CONCLUSIONS: Both SS and MF can have phenotypic features of any of the major naïve/memory T-cell subsets, which questions the current principle of "cell-of-origin" distinction between SS and MF. Phenotypic shifts within these subsets are common, suggesting a functional state rather than a cell-of-origin surrogate. © 2018 International Clinical Cytometry Society.

Simultaneous Cell Detection and Classification in Bone Marrow Histology Images.

Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks: one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage. In this paper, we propose a synchronized deep autoencoder network for simultaneous detection and classification of cells in bone marrow histology images. The proposed network uses a single architecture to detect the positions of cells and classify the detected cells, in parallel. It uses a curve-support Gaussian model to compute probability maps that allow detecting irregularly shape cells precisely. Moreover, the network includes a novel neighborhood selection mechanism to boost the classification accuracy. We show that the performance of the proposed network is superior than traditional deep learning detection methods and very competitive compared to traditional deep learning classification networks. Runtime comparison also shows that our network requires less time to be trained.

[Bipartite organization of human lymphopoiesis]. / Organisation bipartite de la lymphopoïèse humaine.

Due to difficulties to access primary bone marrow samples, human hematopoiesis has long remained far less characterized than in the mouse. Using an in vivo modeling approach of fetal hematopoiesis in humanized mice, we recently showed that human lymphoid cells stem from two functionally specialized populations of CD127- and CD127+ early lymphoid progenitors (ELP) that differentiate independently, respond differently to growth factors, undergo divergent modes of lineage restriction and generate distinct lymphoid populations. Our results demonstrate that, conversely to the mouse, human lymphopoiesis displays a bipartite developmental architecture.

The Human Cell Atlas bone marrow single-cell interactive web portal.

The Human Cell Atlas (HCA) is expected to facilitate the creation of reference cell profiles, marker genes, and gene regulatory networks that will provide a deeper understanding of healthy and disease cell types from clinical biospecimens. The hematopoietic system includes dozens of distinct, transcriptionally coherent cell types, including intermediate transitional populations that have not been previously described at a molecular level. Using the first data release from the HCA bone marrow tissue project, we resolved common, rare, and potentially transitional cell populations from over 100,000 hematopoietic cells spanning 35 transcriptionally coherent groups across eight healthy donors using emerging new computational approaches. These data highlight novel mixed-lineage progenitor populations and putative trajectories governing granulocytic, monocytic, lymphoid, erythroid, megakaryocytic, and eosinophil specification. Our analyses suggest significant variation in cell-type composition and gene expression among donors, including biological processes affected by donor age. To enable broad exploration of these findings, we provide an interactive website to probe intra-cell and extra-cell population differences within and between donors and reference markers for cellular classification and cellular trajectories through associated progenitor states.

Stable overexpression of p130/E2F4 affects the multipotential abilities of bone-marrow-derived mesenchymal stem cells.

Bone-marrow-derived mesenchymal stem cells (MSCs) have great potential in transplantation medicine due to their multiple advantages. However, the controlled differentiation of MSCs is one of the key aspects of effective clinical transplantation. Growing evidence suggests that the cell cycle plays an important role in regulating differentiation, while p130 and E2F4 are key to cell cycle checkpoints. The aim of the study is to evaluate the effects and mechanism of p130/E2F4 on the multidifferentiation of MSCs. Our data showed that the transduction efficiencies of p130 or E2F4 mediated by lentiviral vectors were 80.3%-84.4%. p130 and E2F4 mRNA expression was significantly higher in MSC-p130 and MSC-E2F4 cells than in MSC normal control (NC) cells. Similar results were also observed for p130 and E2F4 protein expression. After osteogenic or adipogenic differentiation, the G1 phase was significantly delayed in the MSC-p130 and MSC-E2F4 groups compared with that in the MSC-NC group. However, the G1 phase in the MSC-p130 and MSC-E2F4 groups did the opposite after chondrogenic differentiation. Moreover, overexpressing p130 or E2F4 significantly improved osteogenic differentiation while inhibiting adipogenic and chondrogenic differentiation of mouse MSCs (mMSCs). Moreover, overexpressing p130 or E2F4 significantly improved migration but not proliferation of mMSCs. Our data suggest that cell cycle regulation may be involved in p130/E2F4-mediated changes in the multipotential abilities of bone-marrow-derived mMSCs.

Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.

BACKGROUND: Morphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast. METHODS: Fifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell. RESULTS: Three binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0). CONCLUSIONS: The CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.

Restoring the quantity and quality of elderly human mesenchymal stem cells for autologous cell-based therapies.

BACKGROUND: Degenerative diseases are a major public health concern for the aging population and mesenchymal stem cells (MSCs) have great potential for treating many of these diseases. However, the quantity and quality of MSCs declines with aging, limiting the potential efficacy of autologous MSCs for treating the elderly population. METHODS: Human bone marrow (BM)-derived MSCs from young and elderly donors were obtained and characterized using standard cell surface marker criteria (CD73, CD90, CD105) as recommended by the International Society for Cellular Therapy (ISCT). The elderly MSC population was isolated into four subpopulations based on size and stage-specific embryonic antigen-4 (SSEA-4) expression using fluorescence-activated cell sorting (FACS), and subpopulations were compared to the unfractionated young and elderly MSCs using assays that evaluate MSC proliferation, quality, morphology, intracellular reactive oxygen species, ß-galactosidase expression, and adenosine triphosphate (ATP) content. RESULTS: The ISCT-recommended cell surface markers failed to detect any differences between young and elderly MSCs. Here, we report that elderly MSCs were larger in size and displayed substantially higher concentrations of intracellular reactive oxygen species and ß-galactosidase expression and lower amounts of ATP and SSEA-4 expression. Based on these findings, cell size and SSEA-4 expression were used to separate the elderly MSCs into four subpopulations by FACS. The original populations (young and elderly MSCs), as well as the four subpopulations, were then characterized before and after culture on tissue culture plastic and BM-derived extracellular matrix (BM-ECM). The small SSEA-4-positive subpopulation representing ~ 8% of the original elderly MSC population exhibited a "youthful" phenotype that was similar to that of young MSCs. The biological activity of this elderly subpopulation was inhibited by senescence-associated factors produced by the unfractionated parent population. After these "youthful" cells were isolated and expanded (three passages) on a "young microenvironment" (i.e., BM-ECM produced by BM cells from young donors), the number of cells increased ≈ 17,000-fold to 3 × 109 cells and retained their "youthful" phenotype. CONCLUSIONS: These results suggest that it is feasible to obtain large numbers of high-quality autologous MSCs from the elderly population and establish personal stem cell banks that will allow serial infusions of "rejuvenated" MSCs for treating age-related diseases.

One tube with eight antibodies for 14-part bone marrow leukocyte differential using flow cytometry.

BACKGROUND: Bone marrow analysis by flow cytometry is part of the routine diagnosis of hematological disorders in medical laboratories. Differential leukocyte count and identification of abnormal cell subsets is currently performed through morphological examination on bone marrow smears by skilled cytologists. In this work, we propose a single 8-color tube for providing equivalent information, using flow cytometry. METHODS: 99 bone marrow samples were classified into 2 groups, (i) 51 samples, obtained from either healthy donors (n = 4) or patients with various diseases at diagnosis or during remission that did not present a hematological malignancy (n = 47), and (ii) 48 pathological samples with quantitative and/or qualitative abnormalities. A panel of eight antibodies-CD3-FITC/CD10-PE/CD38-PerCP-Cy5.5/CD19-PECy7/CD36-APC/CD16-APC-H7/CD34-BV421/CD45-V500-was tested to identify the main cell subsets at different stages of maturation using a FACSCanto-II analyzer. RESULTS: We first proposed a strategy of sequential gating leading to the identification of 14 leukocyte subsets, that is, erythroblasts, monocytes, B-lymphoid cells from hematogones to plasma-cells (5 subsets), T- and NK-cells, polymorphonuclear cells (neutrophils, eosinophils, and basophils), myeloblasts and other immature granular cells. This approach was validated by comparing flow cytometry and microscopic morphological examination, both in cases of normal and abnormal samples. Interestingly, cell identification, and numeration by flow cytometry was easy to perform and highly reproducible. CONCLUSION: A very simple, rapid, and reproducible flow cytometric approach, using a combination of eight antibodies allows determination of the cellular composition of bone marrow with high precision. © 2016 International Clinical Cytometry Society.

Stabilization of pre-optimized multicolor antibody cocktails for flow cytometry applications.

BACKGROUND: Flow cytometry has a multitude of applications in nearly all fields of biology. Newly described biological markers enable the creation of novel reagents which then aid in the elucidation of unique subsets of cells and their potential role in health and disease. In order to enable the simultaneous detection of an even greater number of parameters, the future progress of flow cytometry relies on advances in instrument engineering and the parallel development of new fluorophores. METHODS: In order to address the issues of reagent reliability, reproducibility, and work-flow optimization, we have used the freeze-dry technique to stabilize pre-mixed, pre-optimized, multicolor 'cocktails' of antibodies within 12 × 75 mm flow cytometry tubes (Lyotube). In this study we describe several lyophilized stabilized reagent combinations that are functional for extended periods of time (18 months and beyond), and can be stored at ambient temperature, eliminating cold-chain requirements during transportation and storage. This improves precision and reduces the redundant labor and error-potential associated with mixing antibodies to create "home-brew" cocktails. RESULTS: We have stained different types of samples including normal and leukemic whole blood, bone marrow, and PBMCs, as well as cell lines, directly with BD Lyotube reagents: The data show comparable and consistent performance of multiple batches of dehydrated, stabilized mixtures of antibodies and their liquid counterparts. CONCLUSIONS: The approach we describe here, the Lyotube, facilitates the improvement and implementation of standardization measures in clinical settings and in multi-site studies, a useful tool which can also be applied to determining the efficacy and safety of candidate therapeutics and vaccines. © 2016 International Clinical Cytometry Society.

Advances in bone marrow stem cell therapy for retinal dysfunction.

The most common cause of untreatable vision loss is dysfunction of the retina. Conditions, such as age-related macular degeneration, diabetic retinopathy and glaucoma remain leading causes of untreatable blindness worldwide. Various stem cell approaches are being explored for treatment of retinal regeneration. The rationale for using bone marrow stem cells to treat retinal dysfunction is based on preclinical evidence showing that bone marrow stem cells can rescue degenerating and ischemic retina. These stem cells have primarily paracrine trophic effects although some cells can directly incorporate into damaged tissue. Since the paracrine trophic effects can have regenerative effects on multiple cells in the retina, the use of this cell therapy is not limited to a particular retinal condition. Autologous bone marrow-derived stem cells are being explored in early clinical trials as therapy for various retinal conditions. These bone marrow stem cells include mesenchymal stem cells, mononuclear cells and CD34+ cells. Autologous therapy requires no systemic immunosuppression or donor matching. Intravitreal delivery of CD34+ cells and mononuclear cells appears to be tolerated and is being explored since some of these cells can home into the damaged retina after intravitreal administration. The safety of intravitreal delivery of mesenchymal stem cells has not been well established. This review provides an update of the current evidence in support of the use of bone marrow stem cells as treatment for retinal dysfunction. The potential limitations and complications of using certain forms of bone marrow stem cells as therapy are discussed. Future directions of research include methods to optimize the therapeutic potential of these stem cells, non-cellular alternatives using extracellular vesicles, and in vivo high-resolution retinal imaging to detect cellular changes in the retina following cell therapy.