Here, we present an immunophenotyping strategy for the characterization of megakaryocyte differentiation, and show how that strategy allows the sorting of megakaryocytes at different stages with a fluorescence-activated cell sorter. The methodology can be applied to human primary tissues, but also to megakaryocytes generated in culture in vitro.
Megakaryocyte (MK) differentiation encompasses a number of endomitotic cycles that result in a highly polyploid (reaching even >64N) and extremely large cell (40-60 µm). As opposed to the fast-increasing knowledge in megakaryopoiesis at the cell biology and molecular level, the characterization of megakaryopoiesis by flow cytometry is limited to the identification of mature MKs using lineage-specific surface markers, while earlier MK differentiation stages remain unexplored. Here, we present an immunophenotyping strategy that allows the identification of successive MK differentiation stages, with increasing ploidy status, in human primary sources or in vitro cultures with a panel integrating MK specific and non-specific surface markers. Despite its size and fragility, MKs can be immunophenotyped using the above-mentioned panel and enriched by fluorescence-activated cell sorting under specific conditions of pressure and nozzle diameter. This approach facilitates multi-Omics studies, with the aim to better understand the complexity of megakaryopoiesis and platelet production in humans. A better characterization of megakaryopoiesis may pose fundamental in the diagnosis or prognosis of lineage-related pathologies and malignancy.
Megakaryocytes (MKs) develop from hematopoietic stem cells (HSCs) following a complex process called megakaryopoiesis, which is orchestrated mainly by the hormone thrombopoietin (TPO). The classical view of megakaryopoiesis describes the cellular journey from HSCs through a succession of hierarchical stages of committed progenitors and precursor cells, leading ultimately to a mature MK. During maturation, MKs experience multiple rounds of endomitosis, develop an intricate intracellular demarcation membrane system (DMS), which provides enough membrane surface for platelet production, and efficiently produce and pack the plethora of factors that are contained in the different granules inherited by mature platelets1,2,3. As a result, mature MKs are large cells (40-60 µm) characterized by a highly polyploid nucleus (reaching even >64N). Recent studies suggest alternative routes by which HSCs differentiate into MKs bypassing traditional lineage commitment checkpoints in response to certain physio-pathological conditions4,5,6,7,8,9,10,11. These findings highlight that hematopoietic differentiation towards the mature MK is a continuum and adaptive process that responds to biological needs.
With the increasing knowledge on the cell biology and the molecular aspects characterizing megakaryopoiesis12, most of the research dedicated to the study of the process by flow cytometry are limited to the identification of mature MKs using lineage-specific surface markers (i.e., CD42A/B, CD41/CD61), while earlier MK differentiation stages remain unexplored. We previously documented a strategy to stage megakaryopoiesis in mouse bone marrow and bone marrow-derived MK cultures13,14, which we have adapted and applied to humans15. In the present article we show an immunophenotyping strategy that allows the characterization of megakaryopoiesis, from HSCs to mature MKs, in human primary sources (bone marrow -BM- and peripheral blood -PB-) or in vitro cultures using a panel integrating MK specific and non-specific surface markers (CD61, CD42B, CD49B, CD31, KIT and CD71, amongst others). Despite its large size and fragility, MKs can be immunophenotyped using the above-mentioned cell surface markers and enriched by fluorescence-activated cell sorting under specific conditions of pressure and nozzle diameter to minimize cell rupture and/or damage. This technique facilitates multi-Omics approaches, with the aim to better understand the complexity of megakaryopoiesis and platelet production in human health and disease. Noteworthy, it will pose as a useful tool to aid diagnosis and prognosis in a clinical context of growing demand.
In this manuscript we document a strategy to stage human megakaryopoiesis with a panel integrating MK-specific and non-specific surface markers from primary sources or generated in vitro. Additionally, we provide a protocol to sort, with a fluorescence-activated cell sorter, the preferred fractions and mature MKs (Figure 1). This step is not popular, as it is technically difficult due to the large size and fragility of MKs. However, it has been employed both in mouse and human bone marrow samples previously, and due to technological advancement, with a better result each time16,17,18. Human primary sources where MKs or MK precursors can be studied include bone marrow, cord blood and peripheral blood, amongst other. The proper sample processing to isolate the relevant cell fraction for analysis on each sample is of importance. Standard procedures are incorporated, with some considerations to take into account when aiming at the study of megakaryopoiesis.
Whole blood and bone marrow samples were obtained and processed in accordance with the 1964 Declaration of Helsinki. Whole blood samples were obtained from healthy donors after giving informed consent (ISPA), within a study approved by our institutional medical ethical committee (Hospital Universitario Central de Asturias -HUCA-). Bone marrow samples were obtained from bone marrow aspirate discard material of patients managed at the Dept. of Hematology of the Hospital Clínico San Carlos (HCSC).
Figure 1: Schematic representation of the protocol documented in this manuscript. The primary human sources or primary cultures where MK differentiation can be staged by using immunophenotyping are indicated. This immunophenotyping strategy can be applied to the study of the process in different lineage-related pathologies or malignancy in primary sources. In addition, it makes possible the cell sorting of MKs and precursors with a fluorescence-activated cell sorter, which allows further analysis of enriched fractions. Images used are part of Servier Medical Art (SMART) by Servier and are licensed under CC BY 3.0. Please click here to view a larger version of this figure.
1. Whole blood and bone marrow processing prior to immunophenotyping
2. In vitro MK differentiation from PBMCs
NOTE: MKs can be differentiated in vitro from earlier precursors, such as CD34+ cells, present in different primary sources (i.e., WB/PBMCs, cord blood, bone marrow) and from iPSCs. There are different protocols that have been applied to this end. Here, we use a culture method developed by us that allows MK differentiation from PBMCs, without the need of enriching for CD34+ precursors15,19,20,21,22.
Figure 2: Schematic representation of the PBMC-derived MK culture method. PBMCs from healthy donors were cultured according to the three-phase protocol developed by us to generate MK in vitro (scheme adapted from Salunkhe et al).15 Pictures taken at day 10 and day 13 of culture are shown. Pictures are taken with a 20X objective. Please click here to view a larger version of this figure.
3. Immunophenotyping of MK differentiation – incubation with a panel of tagged-antibodies
Table 1: Notes on cell surface markers of the megakaryocytic lineage Please click here to download this Table.
Table 2: Antibody panels Please click here to download this Table.
4. Ploidy analysis combined with 6-color panels
5. MK differentiation analysis
NOTE: We have seen that the combination of CD31/CD71 allows to set a number of gates which correspond to different stages of MK differentiation. Further back-gating with MK-specific markers allows the separation of mature and immature MKs. Furthermore, in fresh samples, back-gating to verify the presence of other markers used, or to place the populations in the Forward/Side Scatter axes, refines the assessment of MK differentiation stages and allows to discard other cell types that could be present on the same populations.
6. MK and MK precursor cell sorting
NOTE: The stained cells were analyzed and sorted on a fluorescence-activated cell sorter FACS Aria IIu equipped with 488-nm and 633-nm standard solid-state lasers using FACSDiva software; data were additionally analyzed and presented using FlowJo software and Cytobank (viSNE analysis). Purity of sorted fractions was confirmed by flow cytometry analysis of each of the sorted fractions (purity above 85%).
Figure 3: Schematic representation of the principle of fluorescence-activated cell sorting (FACS). The particles go through the 130 µm-nozzle and are forced to break up into a stream of regular droplets due to the application of vibration to the nozzle. Next, the droplets are interrogated by the laser (point of analysis) and the signals are processed to give the ''sort decision" by applying a charge to those droplets. When a charge droplet passes through a high voltage electrostatic field (detection plate), it is deflected and collected into the corresponding collection tube. Please click here to view a larger version of this figure.
7. Post-sort sample preparation
Bone Marrow and Ploidy
In Figure 4, we show a representative immunophenotyping analysis of megakaryopoiesis in BM samples (aspiration) from patients. When plotting the cellular fraction against CD71 and CD31, we have gated six main populations: CD31– CD71– (red), CD31– CD71+ (blue), CD31+ CD71– (orange), CD31+ CD71mid (light green), CD31+ CD71+ (dark green) and CD31++ CD71mid (cream). These populations are not always present in the same positions (considering constant cytometer settings), as it can be seen, and that should be taken into account. This might be inherent to the pathological condition and bone marrow status. Back-gating these six populations overlaid in histograms against a maturation marker contained in the panel (CD42A/CD42B), and to the Forward Scatter, is shown on the right. The figure also shows a plot against CD31/CD71 depicting an overlay of the CD42B+ cells with the total cellular fraction as to visualize the distribution of the more mature MKs. In general terms, the population CD31– CD71– does not contain MKs or lineage precursors, does not present MK maturation markers, and is smaller in size. The CD31– CD71+ population contains mainly cells of the erythroid lineage, although it can also contain common lineage precursors, as we hypothesize from our observations. It remains negative for MK maturation markers, and depending on the proportion of earlier or later erythroid cells, the Forward Scatter might fluctuate as well.
As an example, the myelodysplastic syndrome (MDS) patient has a larger proportion of immature (i.e., larger) erythroid cells, compared to other patients. MK progenitors and mature cells will distribute in the CD31+ cells, with some variation. However, as it can be seen comparing the BM analysis on each pathology, there are some constant features. More mature MKs are present in the CD31++ CD71mid population (cream), and pathologies where this maturation is "blocked" include Immune Thrombocytopenia (ITP) and a reactive BM sample (with underlying inflammation). While in the lymphoma patient there seems to be an equilibrium between early precursors and late MKs (orange and cream gates), this proportion is altered in the MDS patient (with more "blocked" precursors) and in the acute myeloid leukemia (AML) patient (with more mature MKs). Of interest, the "blockade" seems to be different comparing pathologies amongst themselves: in pathologies concurring with underlying inflammation the blockade might be due to a combination of maturation defects, destruction (i.e., autoimmunity) and platelet production by MK rupture.
Figure 4: Immunophenotyìng of megakaryopoiesis in human bone marrow samples from patients with different pathologies. Representative bone marrow (BM) samples from patients diagnosed with lymphoma (i.e., normal BM), myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), immune thrombocytopenia (ITP) and a patient with a reactive BM due to an infection were analysed. When plotting the cellular fraction (i.e., nucleated cells) against CD71 and CD31, we have gated six main populations: CD31– CD71– (red), CD31– CD71+ (blue), CD31+ CD71– (orange), CD31+ CD71mid (light green), CD31+ CD71+ (dark green) and CD31++ CD71mid (cream). These populations are not always present in the same positions (considering steady cytometer settings), as it can be seen, and that should be taken into account. Back-gating these six populations overlaid in histograms against a maturation marker contained in the panel (CD42A/CD42B), and to the Forward Scatter, is shown on the right. The figure also shows a plot against CD31/CD71 depicting an overlay of the CD42B+ cells with the total cellular fraction as to visualize the distribution of the more mature MKs. Numbers matching the gates in the dot plot and the histogram overlays are depicted. Please click here to view a larger version of this figure.
We next set out to analyze the ploidy status of the above-mentioned populations on human BM samples. Figure 5 shows an increasing ploidy status within the CD31+ populations, that we foresee will be different and characteristic for each pathological context. Note that due to the cell fixation and permeabilization used in these experiments, the dot plots are not completely comparable to those of unfixed, unpermeabilized samples (Figure 4).
Figure 5: Ploidy analysis of the populations selected based on CD31/CD71 expression, in human BM samples. Representative bone marrow (BM) samples from patients diagnosed with acute myeloid leukemia (AML) and immune thrombocytopenia (ITP). When plotting the cellular fraction (i.e., nucleated cells) against CD71 and CD31, we have gated six main populations: CD31– CD71– (red), CD31– CD71+ (blue), CD31+ CD71– (orange), CD31+ CD71mid (light green), CD31+ CD71+ (dark green) and CD31++ CD71mid (cream). These populations are not always present in the same positions (considering steady cytometer settings), as it can be seen, and that should be taken into account. Back-gating these six populations overlaid in histograms against Hoechst 33342 shows the different ploidy status of the populations, and the general tendency to increase ploidy with maturation (although MKs can reach maturity independently of the polyploidization status). Numbers matching the gates in the dot plot and the histogram overlays are depicted. Please click here to view a larger version of this figure.
PBMCs and Cell Culture
In Figure 6, we show a representative immunophenotyping analysis of PBMCs and the MK culture set with those PBMCs at day 7 and day 11. We use a panel containing the Lin cocktail, and we show the cellular fraction of live cells before and after Lin– selection. This negative selection might result in the loss of some fraction of MKs co-expressing, for example, CD14, but it also allows a more refined analysis. When plotting the Lin– fraction against CD71 and CD31, we gated five main populations: CD31– CD71–, CD31– CD71+, CD31+ CD71–, CD31+ CD71mid and CD31++ CD71mid. These populations are not always present in the same positions (considering constant cytometer settings), as it can be seen, and that should be taken into account. In this case, the differences are not only inherent to the individual health or pathological status, but also to the MK differentiation capacity of the precursors in the PBMC fraction. Back-gating these five populations overlaid in histograms against other markers contained in the panel (KIT and CD42B), and to the Forward Scatter, is shown on the right. MK precursors and MKs at different stages of maturation are within the CD31+ populations.
Figure 6: Immunophenotyping of megakaryopoiesis during MK cell culture, including the starting PBMC material. Representative immunophenotyping analysis of PBMCs and the MK culture set with those PBMCs at day 7 and day 11. We use a panel containing the Lin cocktail, and we show the cellular fraction (i.e., nucleated cells) before and after Lin- selection. This negative selection might result in the loss of some fraction of MKs co-expressing, for example, CD14, but it also allows a more refined analysis. When plotting the Lin- fraction against CD71 and CD31, we gated five main populations: CD31– CD71–, CD31– CD71+, CD31+ CD71–, CD31+ CD71mid and CD31++ CD71mid. These populations are not always present in the same positions (considering steady cytometer settings), as it can be seen, and that should be taken into account. Back-gating these five populations overlaid in histograms against other markers contained in the panel (KIT and CD42B), and to the Forward Scatter, is shown on the right. MK precursors and MKs at different stages of maturation are within the CD31+ populations. The figure also shows a plot against CD31/CD71 depicting an overlay of the CD42B+ cells with the total cellular fraction as to visualize the distribution of the more mature MKs. Numbers matching the gates in the dot plot and the histogram overlays are depicted. Please click here to view a larger version of this figure.
Table 3: Population percentages of flow cytometry analyses Please click here to download this Table.
In PBMCs, MKs are within the CD31++ CD71mid gate, although their size is not as large as we have seen with cultured MKs. This might be due to either one of the two following options: MKs in PBMCs represent a fraction of immature MKs that enter the circulation, or a fraction of mature circulating MKs that have lost cytoplasmic complexity. Our sort experiments suggest that the latter might be the reason explaining that size difference. Supporting this notion, the expression of CD42B, is not present in 100% of the cells on those populations (as it can also be seen in BM samples in Figure 4), probably due to the loss of surface markers upon proplatelet formation and/or platelet shedding. However, in cultured samples, the MKs in the "mature" gates, are almost 100% CD42B+, and larger in size. These hypothesized cellular dynamics should be further studied and validated.
Due to the pleiotropic effects of TPO, these cultures are heterogeneous and asynchronous, which per se makes it difficult to further analyze discrete MK differentiation stages.19,20 Following the expansion of earlier progenitors, MKs at different maturation stages will appear in the culture from day 6-10 (or earlier, due to donor variability) and will gradually increase in numbers as the MK lineage-committed cells mature towards MK. Some MKs will undergo terminal differentiation and start forming proplatelets. Towards the end of the culture, there will be a gradual increment of cell loss due to "extenuation/exhaustion" after proplatelet formation and platelet shedding or cell death (Figure 2).
If using TPO analogues instead of recombinant TPO, the concentrations should be tested.
The source of progenitors (adult, cord blood) will condition the cultures, as MKs from sources of different developmental stages have their own characteristics. Furthermore, cultures from enriched CD34+ progenitors, while appearing more homogeneous at the beginning of the culture, will reach a stage of heterogeneity and asynchronicity once MK commitment and differentiation commences19,20.
MK Cell Sorting
In Figure 7, we show a representative gating strategy of a sample of MK in vitro culture at day 10 of differentiation. When plotting the nucleated cell fraction against CD31 and CD71, or the Forward Scatter, we can distinguish two main populations characterized by the presence or absence of CD31.
When plotting the CD31+ fraction against CD41 and CD71, we can gate four main populations: CD41– CD71– (1), CD41low CD71+ (2), CD41low CD71+++ (3) and CD41+++ CD71+ (4), in which other surface markers (KIT and CD49B) were expressed differentially, allowing the identification of the MK compartment as CD41 highly positive cells (Figure 7A). The purity of the sorted fractions is shown, as well as cytological stainings of cytospins of the sorted fractions.
In order to characterize the cell population spectrum in PBMC-MK cultures, viSNE analysis was employed (Figure 7B). The viSNE map separates cells into spatially distinct subsets based on the combination of markers that they express. Each point in the viSNE analysis represents an individual cell colored according to the expression levels of CD31, CD42A, CD71 and KIT.
Double negative cells (CD31– and CD71–) are mainly residual lymphocytes (population 5) that persist throughout the culture in these conditions. This population has been sorted in a second round of cell sorting using the remaining fraction of the sample.
Gates 6 and 7 are CD71mid and high expressing cells, which include erythroid progenitors and, potentially, other precursors.
We must take into account the technical difficulties due to the cell heterogeneous nature of this culture method and the sticky nature of mature MKs that results in the presence of mature MKs in subpopulations where they do not belong (see remarks section).
Figure 7: Pre-sort immunophenotypic analysis of 10-day in vitro culture MKs. A) Representative gating strategy using CD31, CD41 and CD71, with two sorting rounds, resulting in CD31+ populations 1-4 and CD31– populations 5-7. The purity of each sorted fraction was analysed after sorting, and is depicted together with a representative microphotograph of stained cytospins from each sorted fraction. B) viSNE map of day 10 PBMC-derived MK culture cells measured by flow cytometry. The map is built using the expression levels of CD31, CD42B, CD71 and KIT, as measured by flow cytometry. Please click here to view a larger version of this figure.
Post-sort processing
The sorted populations can be analyzed by different methods, including cytological and molecular analysis by immunofluorescence (see section 7) in order to evaluate the MK features in a specific physio-pathological context, or to better study the process of megakaryopoiesis. Some examples are presented in Figure 8.
Figure 8. Post-sort MK analysis. A) Cytological analysis of sorted MK compartment from a 10-day in vitro culture. May-Grünwald Giemsa staining allows the observation of key features of mature MKs, i.e., pseudopod formation (red arrow), highly polyploid MK (blue arrow). B) Immunofluorescence analysis. Cells were stained with anti-human von Willebrand factor (shown in red, secondary antibody Alexa Fluor 555 Goat anti-mouse IgG), anti-human CD31 or tandem CD41/CD61, both conjugated with FITC (shown in green) and nuclear DNA was labeled with DAPI (shown in blue). Please click here to view a larger version of this figure.
Remarks:
MKs can be sorted from unexpected regions due to the capacity to attach cells to their membrane (Figure 9). This issue can be a problem to get high purity cell subpopulations. To minimize cell-cell aggregates, 1-5 mM EDTA can be added to the panel mix buffers.
Furthermore, when using WB as starting material, we proposed either obtaining PBMCs or lysing RBCs to use the cellular fraction directly. In Figure 10 we show flow cytometry analysis of the two methodologies. On one hand, when lysing RBCs and analyzing the whole cellular compartment, we still have a large amount of neutrophils. These can be filtered out with a specific marker (or using the Lin cocktail strategy). However, we may lose some MKs due to their promiscuous nature related to surface marker expression. On the other hand, PBMC isolation will allow us to get rid of neutrophils as well as RBCs, although we may lose the MKs with higher density (i.e., the ones with more complex cytoplasmic features or more immature). As shown in Figure 10, MKs from lysed WB show a higher expression of maturation markers, which appears lower when isolating PBMCs. The density gradient might result in the loss of the MKs that still contain a complex cytoplasm, while the ones contained in the PBMC fraction, might be already "exhausted" in the circulation after releasing platelets, and thus maintaining the polyploid nucleus within a less complex cytoplasm. They should not be confused with immature MKs. Each procedure has its advantages and its drawbacks.
Figure 9. The sticky nature of MKs. MKs can be found in unexpected gates due to their capacity to attach cells to their membrane. A-C) MKs sorted in populations 1 (B) and 5 (A and C), according the gating strategy for MK sorting. This issue can be a problem to get high purity cell subpopulations. Please click here to view a larger version of this figure.
Figure 10. Flow cytometry immunophenotyping of MKs from PBMCs or WB after lysing the RBCs. A) Gating strategy. B) Size (FSC), cellular complexity (SSC) and expression of CD42A, CD41, CD71 and KIT in MKs as identified in PBMCs or WB. Please click here to view a larger version of this figure.
Most of the research focusing on the study of megakaryopoiesis by flow cytometry is to date limited to the identification of MK subsets using only lineage-specific surface markers (i.e., CD42A/CD42B, CD41/CD61), while earlier MK differentiation stages have been poorly examined. In the present article we show an immunophenotyping strategy to address a comprehensive flow cytometry characterization of human megakaryopoiesis. Overall, we would like to highlight the utility of combining MK specific and non-specific surface markers (Tables 1 and 2) in the same flow cytometry antibody panel for the study of megakaryopoiesis in a more detailed way. Our novel approach could serve as a powerful tool to aid diagnosis and/or prognosis in hematological disorders. We consider that the presented panels and combinations are not definitive. We have observed a very dynamic behavior of surface markers in human primary sources (mainly in BM samples), which is also dependent on the subjacent pathology. More studies are required, and hopefully, the scientific community will start applying this approach as to feedback with observations from studies focusing on a single pathology and how megakaryopoiesis staging may aid at diagnosis or prognosis. The implementation of combined HSC and MK characterization in primary tissues, considering the diverse routes for MK commitment as commented in the introduction section, is of relevance and worth of further developments. The fact that we are also able to detect MKs in PBMCs is quite an observation. We also hypothesize that the analysis of MKs in this primary source will aid in disease diagnosis and prognosis, as a very relevant alternative to BM aspirates, since extraction of a blood sample is not as invasive. That aspect also requires development.
Despite the technical limitations associated to the biological characteristics of MKs (large size, osmotic fragility, stickiness) we were able to sort different maturation stages in several human MK sources, specifically in BM, PBMCs and PBMCs-derived cell cultures. Future studies will be conducted in order to analyze the peculiarities of megakaryopoiesis in other human sources such as cord blood and in different physio-pathological situations. In addition, the differentiation potential of each gated and sorted cell fraction should be evaluated in colony-forming unit (CFU) assays of MK progenitors (CFU-MK) as to objectively assess the differentiation potential of each population in the megakaryopoiesis journey. While surpassing the technical limitations that were once thought as to impede the sort of mature megakaryocytes, such a necessity still demands more perfection. On one hand, single-cell Omics would allow comprehensive studies when applying the immunophenotyping panels, but if physical sort is required, despite the purity of the sorted populations is above 85% (based on surface marker expression), other variables need to be taken into account, which are still difficult to solve. As mentioned, MK cultures are not synchronous and are very heterogeneous. MKs themselves are promiscuous in surface marker expression, sharing markers with other myeloid and non-myeloid hematopoietic and non-hematopoietic cells. Furthermore, they are very difficult to cluster with the morphometric variables (Forward and Side Scatter) as they distribute widely. Further studies are required to refine this application, but with collaborative efforts, we are closer to narrow down megakaryopoiesis.
The authors have nothing to disclose.
We thank Marcos Pérez Basterrechea, Lorena Rodríguez Lorenzo and Begoña García Méndez (HUCA) and Paloma Cerezo, Almudena Payero and María de la Poveda-Colomo (HCSC) for technical support. This work was partially supported by Medical Grants (Roche SP200221001) to A.B., an RYC fellowship (RYC-2013-12587; Ministerio de Economía y Competitividad, Spain) and an I+D 2017 grant (SAF2017-85489-P; Ministerio de Ciencia, Innovación y Universidades, Spain and Fondos FEDER) to L.G., a Severo Ochoa Grant (PA-20-PF-BP19-014; Consejería de Ciencia, Innovación y Universidades del Principado de Asturias, Spain) to P.M.-B. and an intramural postdoctoral grant 2018 (Fundación para la Investigación y la Innovación Biosanitaria de Asturias – FINBA, Oviedo, Spain) to A.A.-H. We thank Reinier van der Linden for sharing his knowledge (and time), especially his wise advice on multi-color tagged-antibody panel mix and single-color bead control preparation.
130 micron Nozzle | BD | 643943 | required for MK sorting |
5810R Centrifuge | Eppendorf | Cell isolation and washes | |
A-4-62 Swing Bucket Rotor | Eppendorf | Cell isolation and washes | |
Aerospray Pro Hematology Slide Stainer / Cytocentrifuge | ELITech Group | Automatized cytology devise, where slides are stained with Mat-Grünwald Giemsa | |
CO2 Incubator Galaxy 170 S | Eppendorf | Cell Incubation | |
Cytospin 4 Cytocentrifuge | Thermo Scientific | To prepare cytospins | |
FACSAria IIu sorter | BD | Lasers 488-nm and 633-nm | |
FACSCanto II flow cytometer | BD | Lasers 488-nm , 633-nm and 405-nm | |
Olympus Microscope BX 41 | Olympus | Microphotographs | |
Olympus Microscope BX 61 | Olympus | Microphotographs | |
Zoe Fluorescent Cell Imager | BioRad | Microphotographs | |
To obtain PBMCs | |||
Lipids Cholesterol Rich from adult bovine serum | Sigma-Aldrich | L4646 | or similar |
Lymphoprep | Stem Cell Technologies | #07801 | or similar |
Penicillin-Streptomycin | Sigma-Aldrich | P4333 | or similar |
Recombinant human Erythropoietin (EPO) | R&D Systems | 287-TC-500 | or similar |
Recombinant human stem cell factor (SCF) | Thermo Fisher Scientific, Gibco™ | PHC2115 | or similar |
Recombinant human thrombopoietin (TPO) | Thermo Fisher Scientific, Gibco™ | PHC9514 | or TPO receptor agonists |
StemSpan SFEM | Stem Cell Technologies | #09650 | |
Flow Cytometry Analyses | |||
Bovine Serum Albumin | Merck | A7906-100G | or similar |
BD CompBead Anti-Mouse Ig, κ/Negative Control Compensation Particles Set | BD | 552843 | Antibodies for human cells are generally from mouse. |
BD Cytofix/Cytoperm | BD | 554714 | or similar |
BD FACS Accudrop Beads | BD | 345249 | |
CD31 AF-647 | BD | 561654 | Mouse anti-human |
CD31 FITC | Immunostep | 31F-100T | |
CD34 FITC | BD | 555821 | Mouse anti-human |
CD41 PE | BD | 555467 | Mouse anti-human |
CD41 PerCP-Cy5.5 | BD | 333148 | Mouse anti-human |
CD42A APC | Immunostep | 42AA-100T | We observed unspecific binding… that needs to be assessed |
CD42A PE | BD | 558819 | Mouse anti-human |
CD42B PerCP | Biolegend | 303910 | Mouse anti-human |
CD49B PE | BD | 555669 | Mouse anti-human |
CD61 FITC | BD | 555753 | Mouse anti-human |
CD71 APC-Cy7 | Biolegend | 334109 | Mouse anti-human |
Hoechst 33342 | Thermo Fisher Scientific | H3570 | |
Human BD Fc Block | BD | 564219 | Fc blocking – control |
KIT PE-Cy7 | Biolegend | 313212 | Mouse anti-human |
Lineage Cocktail 2 FITC | BD | 643397 | Mouse anti-human |
RNAse | Merck | R6513 | or similar |
Triton X-500 | Merck | 93443-500ML | or similar |
Cell strainers for sorting | |||
CellTrics Filters 100 micrometers | Sysmex | 04-004-2328 | Cell strainers |
Note: we do not specify general reagents/chemicals (PBS, EDTA, etc) or disposables (tubes, etc), or reagents specified in previous published and standard protocols – unless otherwise specified. |