Liver injuries are accompanied by progenitor cell expansion that represents a heterogeneous cell population. Novel classification of this cellular compartment allows for the distinguishing of multiple subsets. The method described here illustrates the flow cytometry analysis and high purity isolation of various subsets that can be used for further assays.
During chronic liver injuries, progenitor cells expand in a process called ductular reaction, which also entails the appearance of inflammatory cellular infiltrate and epithelial cell activation. The progenitor cell population during such inflammatory reactions has mostly been investigated using single surface markers, either by histological analysis or by flow cytometry-based techniques. However, novel surface markers identified various functionally distinct subsets within the liver progenitor/stem cell compartment. The method presented here describes the isolation and detailed flow cytometry analysis of progenitor subsets using novel surface marker combinations. Moreover, it demonstrates how the various progenitor cell subsets can be isolated with high purity using automated magnetic and FACS sorting-based methods. Importantly, novel and simplified enzymatic dissociation of the liver allows for the isolation of these rare cell populations with a high viability that is superior in comparison to other existing methods. This is especially relevant for further studying progenitor cells in vitro or for isolating high-quality RNA to analyze the gene expression profile.
Liver regeneration is mostly associated with the self-renewal capacity of hepatocytes. Nevertheless, chronic liver injuries occur with progenitor cell activation and expansion, which have been associated with their ability to differentiate into hepatocytes and cholangiocytes1,2,3,4. This is especially relevant because, during chronic injuries, hepatocyte proliferation is not effective. Despite multiple genetic tracing studies targeting progenitor cells, their role in liver regeneration remains controversial5,6,7,8. Moreover, the activation of progenitor cells has been linked to increased fibrotic response in the liver, which raises questions about their exact role during injuries9,10.
The heterogeneous nature of the progenitor cell compartment has long been suggested by gene expression studies that isolated progenitor cells expressing a single surface marker using microdissection or cell sorting-based methods1,11. Indeed, recently, a novel surface marker combination using gp38 (podoplanin) unequivocally linked previous single markers of progenitor cells to various subsets12. Importantly, these subsets not only differed in their surface marker expression but also exhibited functional alterations during injuries12.
Multiple animal models have been utilized to investigate progenitor cell activation and liver regeneration. It seems that the various injury types promote the activation of differing subsets of progenitor cells12. This might explain the phenotypic divergence of the ductular reaction observed in humans4. Thus, the complex phenotypic and functional analyses of progenitor cells are pivotal to understand their role in injuries and the true significance of the ductular reaction in liver diseases.
Besides surface marker combinations, the crucial differences in cell isolation protocols further complicate the conclusions based on previous studies2. A substantial amount of studies addressed the role of progenitor cells that greatly differ in their isolation protocol (e.g., liver dissociation (enzyme combination and duration of the process), density medium, and centrifugation speed)2. An optimized isolation technique, providing better viability for rare cell populations and reflective of subset composition, has been developed and published recently12. The aim of this article is to provide a more detailed protocol of this liver cell isolation procedure and the subset analysis to allow for the proper reproduction of the technique. Additionally, the protocol includes a comparison with the previous isolation method to demonstrate the differences compared to the new protocol.
All experimental procedures were conducted with the approval of the ethics and animal care committees of Homburg University Medical Center.
1. Preparation of Materials and Buffers
2. Preparation of Liver Single-cell Suspension
3. Determination of the Cell Count Using Flow Cytometry
NOTE: For determining the cell counts, an automated cell counter or, ideally, the flow cytometry-based cell quantification described below is suggested instead of the classical Neubauer chamber-based method. The liver single-cell suspension described in step 2 contains parenchymal and non-parenchymal cells (NPC) with greatly differing sizes and granularities. The proper exclusion of cellular debris together with the gating-on forward scatter, side scatter (FSC-SSC) characteristic of NPCs when using flow cytometry ensures the success of the described protocol12.
4. Staining of the Liver Single-cell Suspension for the Fow Cytometry Analysis of Progenitor Subsets
Antibody | Clone | Host/Isotype | Stock Concentration [mg/mL] | Dilution |
CD64 | X54-5/7.1 | Mouse IgG1, κ | 0.5 | 1:100 |
CD16/32 | 93 | Rat IgG2a, λ | 0.5 | 1:100 |
CD45 | 30-F11 | Rat IgG2b, κ | 0.2 | 1:200 |
CD31 | MEC13.3 | Rat IgG2a, κ | 0.5 | 1:200 |
ASGPR1 | Polyclonal Goat IgG | 0.2 | 1:100 | |
Podoplanin | 1/8/2001 | Syrian Hamster IgG | 0.2 | 1:1,400 |
Podoplanin | 1/8/2001 | Syrian Hamster IgG | 0.5 | 1:1,400 |
CD133 | Mb9-3G8 | Rat IgG1 | 0.03 | 3 µL |
CD133 | 315-2C11 | Rat IgG2a, λ | 0.5 | 1:100 |
CD34 | RAM34 | Rat IgG2a, κ | 0.5 | 1:100 |
CD90.2 | 53-2,1 | Rat IgG2, κ | 0.5 | 1:800 |
CD157 | BP-3 | Mouse IgG2b, κ | 0.2 | 1:600 |
EpCAM | G8.8 | Rat IgG2a, κ | 0.2 | 1:100 |
Sca-1 | D7 | Rat IgG2a, κ | 0.03 | 10 µL |
Mouse IgG2b, κ | MPC-11 | 0.2 | ||
Rat IgG1 | RTK2071 | 0.2 | ||
Rat IgG2b, κ | RTK4530 | 0.2 | ||
Rat IgG2a, κ | RTK2758 | 0.5 | ||
Rat IgG2a, κ | RTK2758 | 0.2 | ||
Syrian Hamster IgG | SHG-1 | 0.2 | ||
Syrian Hamster IgG | SHG-1 | 0.5 | ||
Normal Goat IgG Control | Polyclonal Goat IgG | 1 | ||
Donkey anti-Goat IgG | Donkey IgG | 2 | 1:800 | |
Streptavidin | 1 | 1:400 |
Table 1.
Antibody | 1 | 2 | 3 | 4 | 5 |
CD45 APC/Cy7 | Rat IgG2b, κ 0.5 µL |
+ | + | + | + |
CD31 Biotin | + | Rat IgG2a, κ 0.5 µL |
+ | + | + |
ASGPR1 purified | + | + | Normal Goat IgG Control 0.2 µL |
+ | + |
Podoplanin APC | + | + | + | Syrian Hamster IgG 1 µL of a 1:14 Dilution |
+ |
CD133 PE | + | + | + | + | Rat IgG1 0.45 µL |
Donkey anti-Goat Alexa Fluor 488 |
+ | + | + | + | + |
Streptavidin Alexa Fluor 405 |
+ | + | + | + | + |
Table 2.
5. Magnetic Microbead-based Enrichment of Progenitor Cells
6. Magnetic Microbead-based Automated Cell Purification of Progenitor Cell Subsets Combined from Multiple Livers
NOTE: Since progenitor cell subsets represent rare cell populations, combining cells from multiple livers is often needed to achieve sufficient cell numbers for further experiments. As an example, CD133+ and gp38+ cell separation is described below.
7. Flow Cytometry Cell Sorting
NOTE: A high purity sort of any progenitor cell subset could be achieved with the protocol described below. The overall yield of cells is much lower than that described in step 6 and is best for gene expression analysis.
Parameter | Setting |
Nozzle Size | 85 µm |
Frequency | 46.00 – 46.20 |
Amplitude | 38.30 – 55.20 |
Phase | 0 |
Drop Delay | 28.68 – 28.84 |
Attenuation | Off |
First Drop | 284 – 297 |
Target Gap | 9.-14 |
Pressure | 45 psi |
Table 3.
The procedure presented here for the digestion of the liver using a novel mixture of enzymes results in a single-cell suspension containing parenchymal and non-parenchymal liver cells (Figures 1 and 2a). After the ACK-lysing of red blood cells, the direct flow cytometry analysis of the single-cell suspension is possible (Figures 1 and 2). The gating strategy involves the exclusion of doublets and dead cells (Figure 2a). The cells that are negative for CD45, CD31, and ASGPR1 are gated. This population includes the liver progenitor cells and can be grouped based upon their gp38 and CD133 expression (Figure 2a). The gp38+CD133+ and the gp38–CD133+ cells represent the most abundant cell populations among CD45–CD31–ASGPR1– cells in the healthy adult mouse liver (Figure 2a, b). These subsets differ in the expression of additional surface markers previously associated with progenitor cells, such as Epcam, Sca-1, and CD34 (Figure 2c).
Progenitor cells could be depleted from hematopoietic and parenchymal cells, such as hepatocytes and liver sinusoidal endothelial cells (LSECs) (Figures 1 and 3), and progenitor cells could be further purified with magnetic microbead-based isolation (Figure 3a, b) or with high-purity sorting (Figures 1 and 4). The magnetic microbead-based isolation of CD133+ or gp38+ cells results in over 90% purity (Figure 3a, b) regarding any contaminating CD45, CD31, or ASGPR1+ cells, and the cells remain highly viable (Figure 3a, b).
The major obstacle in progenitor cell biology is that the cells are representing rare cell populations, even in the case of inflammation. On the other hand, the variety of isolation techniques available can differ in the cellular composition and viability of isolated liver cells2. This is especially relevant for the choice of enzyme mixture utilized for tissue dissociation. Here, collagenase P instead of collagenase D was used for the liver1,2,8. Importantly, the expression level of CD133 on progenitor cells and the yield of isolated cell populations were greatly reduced in the presence of collagenase D (Figure 5a, b). In addition to this, our mixture contained dispase, which is highly suitable for gentle disaggregation and subculturing of various cell types13. Collagenase P together with dispase represents an ideal combination for cell dissociation of the liver for progenitor cell analysis. This combination was also superior to trypsin or pronase-based digestion of the liver (data not shown). It is equally important to note that density centrifugation, such as Percoll-based enrichment2,14, was not necessary for the progenitor analyses presented in this protocol.
Figure 1: Workflow of the described protocol. Please click here to view a larger version of this figure.
Figure 2: Novel classification of liver progenitor subsets. A single-cell suspension was prepared from healthy livers and subsequently stained with a panel of surface markers: CD45, CD31, CD133 and gp38 (A). For dead cell exclusion, propidium iodide (PI) was utilized. Representative dot plots with the gating strategy are depicted. The gates used for flow cytometry cell count determination are also marked. (B) The percentages and the absolute number of cells present per g of liver tissue of the various stromal cell subsets among the CD45-negative cells in wild-type untreated animals are shown. (C) The histograms display distinguishing marker characteristics of stromal cell subsets in a healthy liver. High expression of CD90.2 and the presence of CD157 are specific for A, while CD34 is specific for subpopulation B. Sca-1 is present in B, C, D and Epcam is only present in population C and D. Mean ± SEM; the data (A-C) represent 2-3 independent experiments with n = 3-4 per experiment. The data were compared using an unpaired, two-tailed T test * P<0.05, ** P<0.005, *** P<0.0001. Please click here to view a larger version of this figure.
Figure 3: Magnetic microbead-based enrichment and automated cell purification. (A) Magnetic microbead-based enrichment of CD45–, CD31– and ASGPR1– cells are depicted before and after enrichment. (B) Representative dot plots of the automated microbead-based cell isolations are depicted for CD133+ and gp38+ cells, on the left. On the right, the viability of the cell populations before and after enrichment are shown as the percentage of propidium iodide (PI)-negative cells. Mean ± SEM; the data (A-B) represent 3 independent experiments with n = 3 per experiment. The data were compared using an unpaired, two-tailed T test * P<0.05, ** P<0.005, *** P<0.0001. Please click here to view a larger version of this figure.
Figure 4: Flow cytometry sort of progenitor subsets with high purity. Dot plots depict the purity of the cell populations in the sorted samples (post-sort). The data represent 3 independent experiments. Please click here to view a larger version of this figure.
Figure 5: Comparison of digestion enzymes. The single-cell suspension was prepared using collagenase P (as described in step 2) or collagenase D (1 mg/mL + DNase-I 0.1 mg/mL) from healthy livers and was subsequently stained with a panel of surface markers: CD45, CD31, CD133, and gp38 (A). The percentages of cells present per g of liver tissue of the various stromal cell subsets among the CD45-negative cells in wild-type untreated animals are shown. (B) The median fluorescence intensity of CD133 is depicted for the CD133+ cell population. Mean ± SEM; the data (A-B) represent 2 independent experiments with n = 3 per experiment. The data were compared using an unpaired, two-tailed T test * P<0.05, ** P<0.005, *** P<0.0001. Please click here to view a larger version of this figure.
Liver inflammation and injury of different origins trigger regenerative processes in the liver that are accompanied by progenitor cell expansion and activation2,3. These liver progenitor cells possess stem cell characteristics and likely play a significant role in the pathomechanism of various liver diseases.
The heterogeneity of liver progenitor cells has long been suggested. The re-evaluation of liver progenitor subsets using a novel surface marker combination of CD133 and gp38 could identify subsets that carry different surface markers and express a unique set of inflammation-related genes during liver injury12. The method described here allows for the direct flow cytometry analysis of rare cells and their direct comparison in various liver injury models. This is especially relevant, as various injuries trigger the activation of multiple progenitor subsets that could be reflective of the cellular heterogeneity observed in ductular reactions3,4,11,12. Notably, a small fraction of murine liver tissue (0.2 g) is enough to isolate sufficient cells for the flow cytometry analysis of progenitors using the described method.
Flow cytometry sorting providing high purity populations of the subsets is necessary to explore their unique gene expression profiles. Previous reports described flow cytometry-based progenitor cell isolation15,16. Nevertheless, the protocol presented here provides an optimized method that ensures the high purity and viability of these cells. Notably, in vitro culturing and expansion techniques described previously can be nicely combined with the protocol presented here15,16.
Many types of flow cytometry sorters are available at various institutions, and their particular instrumentations might be slightly different. Nevertheless, the general principles for cell sorting are presented in the described protocol. Generally, a lower pressure and bigger nozzle size are absolutely necessary, independent of the machine types and specifications. Moreover, the utilization of an appropriate sorting medium can significantly increase the viability and the RNA quality of the sorted cells, which is an important factor, particularly for gene expression studies. The sorting of progenitor cells, however, greatly reduces cell viability, and the yield of cells after sorting is relatively low (for high purity sort of 7-10,000 events/subpopulation, 4-5 healthy livers need to be pooled). This could be a limiting factor for further in vitro analyses. We suggest magnetic microbead-based isolation for in vitro assays, because of its higher yield and cell viability, and flow cytometry sorting for gene expression analysis, especially when more specified subset analyses and isolations are needed.
Based on the fact that the viability of cells is greatly reduced after flow cytometry sorting, an automated magnetic microbead-based isolation of progenitor cells has been developed and presented here. It allows for the isolation of a larger number of progenitor cells with high purity (combining multiple livers). Furthermore, the viability of the cells is maintained, since the time necessary for the isolation process is greatly reduced. While lower numbers of progenitor cells can be expanded in vitro1,2,15,16, it is recommended to consider how these in vitro manipulations could alter the cellular features of stem/progenitor cells described for other mesenchymal and stromal cell populations17,18.
The major limitation of the presented magnetic microbead-based isolation is that the CD133+ and the gp38+ cell populations represent heterogeneous cells. Additional surface markers might be necessary to identify smaller cell populations.
The understanding of how hepatic stem cells and progenitors relate to each other is far from complete, despite excellent studies by Lola Reid and others1,8,19. Thus, the careful consideration of techniques resulting in higher yields and viability, such as the one suggested here, could help to extend the analyses of progenitor subsets and the understanding of their interconnected relationships.
Overall, we have described the detailed isolation and analysis of liver progenitor subsets that were recently distinguished12. Moreover, by employing a novel enzyme combination, rare progenitors could be analyzed by direct flow cytometry measurements. Additionally, the protocol demonstrates how to enrich progenitor cells with a high purity, using either cell sorting or an automated microbead-based technique.
Troubleshooting:
The percentages of cell debris and dying cells are high
If, during digestion (step 2), the pipetting of the liver samples is too harsh, the percentage of dying cells in the single-cell suspension increases. If the liver is cut into larger pieces than suggested, the digestion is less efficient and results in more cell death during preparation.
After completing the procedure in step 5, the purity of the samples is not sufficient
If the percentages of cell debris and dying cells in the single-cell suspension prepared in step 2 are too high, the microbeads bind unspecifically, and therefore, the purity of the isolation is greatly reduced.
Low cell number after microbead-based purification
For the success of the described protocol, it is important that the ratio of cells to microbeads is optimal, as suggested in the protocol. Utilizing more microbeads reduces the purity and decreases the yield and viability of the isolated cells. If surface markers other than those presented in the protocol are used for progenitor subset isolation, the optimal ratio of cells to microbeads must be determined.
Magnetic microbead based isolation of gp38+CD133– population
Follow steps in section 5. In step 5.3 add additionally 10 µL anti-CD133+ microbeads, then follow steps 5, 6.2 and 6.3 as described.
Calculating absolute cell numbers
The liver weight is used to calculate how many cells of a certain subset are present per gram liver tissue.
(% of the subpopulation in living cells (based on flow cytometry) / 100) x total living cell number isolated from the liver piece = Z
(1/ weight of liver piece) x Z = cell number/g liver tissue
Other cell populations that can be isolated with this method
It is possible to isolate the following liver cells using this digestion protocol: CD45+ cells (or subpopulations of hematopoietic cells, e.g. Kupffer cells, dendritic cells, T cells, NK cells, etc.) and LSECs. The digestion protocol is not suitable for isolation of hepatocytes or hepatic stellate cells. These cells are present at relatively low cell numbers and they show reduced viability compared to other available methods.
The authors have nothing to disclose.
This work was supported by the Alexander von Humboldt Foundation Sofja Kovalevskaja Award to VLK.
RPMI | Life Technologies | 21875-034 | |
phenol red free DMEM | Life Technologies | 31053-028 | |
FBS | Life Technologies | 10270-106 | |
Collagenase P | Sigma-Aldrich | 11249002001 | |
DNAse-I | Sigma-Aldrich | 10104159001 | |
Dispase | Life Technologies | 17105041 | |
ACK Lysing Buffer | Life Technologies | A10492-01 | |
HBSS | Life Technologies | 14025-050 | |
PBS | Sigma-Aldrich | D8537 | |
Sodium Azide | Sigma-Aldrich | S2002 | Prepare 1 % stock solution |
10 % BSA | Miltenyi Biotec | 130-091-376 | |
autoMACS Rinsing Solution | Miltenyi Biotec | 130-091-222 | add 0.5 % (v/v) BSA and store on ice |
Phenol-red free DMEM | Sigma-Aldrich | D1145 | |
counting Beads Count Bright | Life Technologies | C36950 | |
PI | Miltenyi Biotec | 130-093-233 | |
FcR Blocking Reagent | Miltenyi Biotec | 130-092-575 | |
anti-CD31 MicroBeads | Miltenyi Biotec | 130-097-418 | |
anti-CD45 MicroBeads | Miltenyi Biotec | 130-052-301 | |
Dead Cell Removal Kit | Miltenyi Biotec | 130-090-101 | |
anti-Biotin MicroBeads | Miltenyi Biotec | 130-090-485 | |
CD64 Purified | BioLegend | 139302 | Dilution: 1:100 |
CD16/32 Purified | BioLegend | 101302 | Dilution: 1:100 |
CD45 APC/Cy7 | BioLegend | 103116 | Dilution: 1:200, marks hematopoetic cells |
CD31 Biotin | BioLegend | 102504 | Dilution: 1:200, marks endothelial cells |
ASGPR1 Purified | Bio-Techne | AF2755-SP | Dilution: 1:100, marks hepatocytes |
Podoplanin APC | BioLegend | 127410 | Dilution: 1:1400, marks progenior cells |
Podoplanin Biotin | BioLegend | 127404 | Dilution: 1:1400 |
CD133 PE | Miltenyi Biotec | 130-102-210 | use 3 µl, marks progenitor cells |
CD133 Biotin | BioLegend | 141206 | Dilution: 1:100 |
CD34 Biotin | eBioScience | 13-0341-81 | Dilution: 1:100 |
CD90.2 Pacific Blue | BioLegend | 140306 | Dilution: 1:800 |
CD157 PE | BioLegend | 140203 | Dilution: 1:600 |
EpCAM Brilliant Violet 421 | BioLegend | 118225 | Dilution: 1:100 |
Sca-1 Biotin | Miltenyi Biotec | 130-101-885 | use 10 µl |
Mouse IgG2b, κ PE | BioLegend | 400311 | |
Rat IgG1 PE | BioLegend | 400407 | |
Rat IgG2b, κ APC/Cy7 | BioLegend | 400624 | |
Rat IgG2a, κ Biotin | BioLegend | 400504 | |
Rat IgG2a, κ Brilliant Violet 421 | BioLegend | 400535 | |
Syrian Hamster IgG APC | BioLegend | 402012 | |
Syrian Hamster IgG Biotin | BioLegend | 402004 | |
Normal Goat IgG Control Purified | Bio-Techne | AB-108-C | |
Donkey anti-Goat IgG Alexa Fluor 488 | Life Technologies | A11055 | Dilution: 1:800 |
Streptavidin Alexa Fluor 405 | Life Technologies | S32351 | Dilution: 1:400 |
100 µm Filter mesh | A. Hartenstein | PAS3 | |
LS Column | Miltenyi Biotec | 130-042-401 | |
QuadroMACS separator | Miltenyi Biotec | 130-090-976 | |
MACSQuant Analyzer 10 | Miltenyi Biotec | 130-096-343 | |
AutoMACS Pro Separator | Miltenyi Biotec | 130-092-545 | |
FACS AriaTMIII | BD Biosciences | ||
FACSDiva sofware | BD Biosciences | ||
Polypropylene Round bottom tube | Falcon | 352063 | |
Rneasy plus mini kit | Qiagen | 74134 | RLT lysis buffer is included |