Processing fixed and stored adipose-derived stem cells for quantitative protein array assays

Discovery of surface biomarkers for cell mechanophenotype via an intracellular protein-based enrichment strategy

Cellular mechanophenotype is often a defining characteristic of conditions like cancer malignancy/metastasis, cardiovascular disease, lung and liver fibrosis, and stem cell differentiation. However, acquiring living cells based on mechanophenotype is challenging for conventional cell sorters due to a lack of biomarkers. In this study, we demonstrate a workflow for surface protein discovery associated with cellular mechanophenotype. We sorted heterogeneous adipose-derived stem/stromal cells (ASCs) into groups with low vs. high lamin A/C, an intracellular protein linked to whole-cell mechanophenotype. Proteomic data of enriched groups identified surface protein candidates as potential biochemical proxies for ASC mechanophenotype. Select surface biomarkers were used for live-cell enrichment, with subsequent single-cell mechanical testing and lineage-specific differentiation. Ultimately, we identified CD44 to have a strong inverse correlation with whole-cell elastic modulus, with CD44^lo cells exhibiting moduli three times greater than that of CD44^hi cells. Functionally, these stiff and soft ASCs showed enhanced osteogenic and adipogenic differentiation potential, respectively. The described workflow can be replicated for any phenotype with a known correlated intracellular protein, allowing for the acquisition of live cells for further characterization, diagnostics, or therapeutics.

Mass-Added Density Modulation for Sorting Cells Based on Differential Surface Protein Levels

Cell sorting is a powerful tool in basic research and therapeutic enrichment. However, common cell sorting methods, such as fluorescence-activated cell sorting (FACS) and magnetic-activated cell sorting (MACS) have significant limitations, such as generally low cell yields or restriction to binary separation, respectively. To address these limitations, we developed a two-step cell sorting method called mass-added density centrifugation (MADC) to enable nonbinary separation of large cell numbers based on surface protein levels. In the first MADC step (mass-adding), antibody-directed massive microparticles bind target surface proteins to modulate single-cell density proportionally to target protein level. Second, microparticle-laden cells are subjected to discontinuous density gradient centrifugation, whereby they separate into discrete density bands which can be isolated for downstream use. MADC will prove especially advantageous for obtaining sufficient cell numbers for protein analyses from large source populations, and it is a fast process that can facilitate live cell enrichment for therapies that require tens of millions of cells. Here, we demonstrate MADC's utility for both live and fixed cell sorts of multiple cell types based on abundance of an example target protein, CD44. CD44 quantity in separated cell groups was assayed with western blots and correlated with modulated cell density. This novel sorting method enables rapid, nonbinary isolation of large quantities of cells based on surface protein levels and should prove useful in both basic science and therapeutic applications. © 2020 International Society for Advancement of Cytometry.

Generating Cell Type-Specific Protein Signatures from Non-symptomatic and Diseased Tissues

Here we demonstrate a technique to generate proteomic signatures of specific cell types within heterogeneous populations. While our method is broadly applicable across biological systems, we have limited the current work to study neural cell types isolated from human, post-mortem Alzheimer's disease (AD) and aged-matched non-symptomatic (NS) brains. Motivating the need for this tool, we conducted an initial meta-analysis of current, human AD proteomics studies. While the results broadly corroborated major neurodegenerative disease hypotheses, cell type-specific predictions were limited. By adapting our Formaldehyde-fixed Intracellular Target-Sorted Antigen Retrieval (FITSAR) method for proteomics and applying this technique to characterize AD and NS brains, we generated enriched neuron and astrocyte proteomic profiles for a sample set of donors (available at www.fitsarpro.appspot.com ). Results showed the feasibility for using FITSAR to evaluate cell-type specific hypotheses. Our overall methodological approach provides an accessible platform to determine protein presence in specific cell types and emphasizes the need for protein-compatible techniques to resolve systems complicated by cellular heterogeneity.