A major challenge for expanding specific types of hematopoietic cells ex vivo for the treatment of blood cell pathologies is identifying the combinations of cellular and matrix cues that direct hematopoietic stem cells (HSC) to self-renew or differentiate into cell populations ex vivo. Microscale screening platforms enable minimizing the number of rare HSCs required to screen the effects of numerous cues on HSC fate decisions. These platforms create a strong demand for label-free methods that accurately identify the fate decisions of individual hematopoietic cells at specific locations on the platform. We demonstrate the capacity to identify discrete cells along the HSC differentiation hierarchy via multivariate analysis of Raman spectra. Notably, cell state identification is accurate for individual cells and independent of the biophysical properties of the functionalized polyacrylamide gels upon which these cells are cultured. We report partial least-squares discriminant analysis (PLS-DA) models of single cell Raman spectra enable identifying four dissimilar hematopoietic cell populations across the HSC lineage specification. Successful discrimination was obtained for a population enriched for long-term repopulating HSCs (LT-HSCs) versus their more differentiated progeny, including closely-related short-term repopulating HSCs (ST-HSCs), and fully differentiated lymphoid (B cells) and myeloid (granulocytes) cells. The lineage-specific differentiation states of cells from these four sub-populations were accurately identified independent of the stiffness of the underlying biomaterial substrate, indicating subtle spectral variations that discriminated these populations were not masked by features from the culture substrate. This approach enables identifying the lineage-specific differentiation stages of hematopoietic cells on biomaterial substrates of differing composition, and may facilitate correlating hematopoietic cell fate decisions with the extrinsic cues that elicited them.

A major challenge to experimental studies and therapeutic uses of hematopoietic stem cells (HSC) is the limited options for analytical tools that can reliably resolve functional differences in heterogeneous HSC subpopulations at the single cell level. Currently available methods require the use of external labels and/or separate clonogenic and transplantation assays to identify bona fide stem cells, necessitating the harvest of bulk cell populations and long incubation times that obscure how individual HSCs dynamically respond to exogenous and endogenous stimuli. In this study, we employ Raman spectroscopy to noninvasively resolve the dynamics of individual differentiating hematopoietic progenitor cells during the course of neutrophilic differentiation. We collected Raman peaks of individual cells daily over the course of 14-day neutrophilic differentiation. Principal component analysis (PCA) of the Raman peaks revealed spectral differences between individual cells during differentiation that were strongly correlated with changes in the nucleus shape and surface antigen expression, the primary traditional means of monitoring neutrophilic differentiation. Additionally, our results were consistently reproducible in independent rounds of neutrophilic differentiation, as demonstrated by our partial least-squares discriminant analysis (PLS-DA) of the Raman spectral information that predicted the degree of neutrophilic differentiation with high sensitivity and specificity. Our findings highlight the utility and reliability of Raman spectroscopy as a robust molecular imaging tool to monitor the kinetics of HSC differentiation patterns.

Identifying the matrix properties that permit directing stem cell fate is critical for expanding desired cell lineages ex vivo for disease treatment. Such efforts require knowledge of matrix surface chemistry and the cell responses they elicit. Recent progress in analyzing biomaterial composition and identifying cell phenotype with two label-free chemical imaging techniques, TOF-SIMS and Raman spectroscopy are presented. TOF-SIMS is becoming indispensable for the surface characterization of biomaterial scaffolds. Developments in TOF-SIMS data analysis enable correlating surface chemistry with biological response. Advances in the interpretation of Raman spectra permit identifying the fate decisions of individual, living cells with location specificity. Here we highlight this progress and discuss further improvements that would facilitate efforts to develop artificial scaffolds for tissue regeneration.

The cellular and matrix cues that induce stem cell differentiation into distinct cell lineages must be identified to permit the ex vivo expansion of desired cell populations for clinical applications. Combinatorial biomaterials enable screening multiple different microenvironments while using small numbers of rare stem cells. New methods to identify the phenotypes of individual cells in cocultures with location specificity would increase the efficiency and throughput of these screening platforms. Here, we demonstrate that partial least-squares discriminant analysis (PLS-DA) models of calibration Raman spectra from cells in pure cultures can be used to identify the lineages of individual cells in more complex culture environments. The calibration Raman spectra were collected from individual cells of four different lineages, and a PLS-DA model that captured the Raman spectral profiles characteristic of each cell line was created. The application of these models to Raman spectra from test sets of cells indicated individual, fixed and living cells in separate monocultures, as well as those in more complex culture environments, such as cocultures, could be identified with low error. Cells from populations with very similar biochemistries could also be identified with high accuracy. We show that these identifications are based on reproducible cell-related spectral features, and not spectral contributions from the culture environment. This work demonstrates that PLS-DA of Raman spectra acquired from pure monocultures provides an objective, noninvasive, and label-free approach for accurately identifying the lineages of individual, living cells in more complex coculture environments.

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