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Background Clinical translation of mesenchymal stromal cells (MSCs) necessitates basic characterization of the cell product since variability in biological source and processing of MSCs may impact therapeutic outcomes. Although expression of classical cell surface markers (e.g., CD90, CD73, CD105, and CD44) is used to define MSCs, identification of functionally relevant cell surface markers would provide more robust release criteria and options for quality control. In addition, cell surface expression may distinguish between MSCs from different sources, including bone marrow-derived MSCs and clinical-grade adipose-derived MSCs (AMSCs) grown in human platelet lysate (hPL). Methods In this work we utilized quantitative PCR, flow cytometry, and RNA-sequencing to characterize AMSCs grown in hPL and validated non-classical markers in 15 clinical-grade donors. Results We characterized the surface marker transcriptome of AMSCs, validated the expression of classical markers, and identified nine non-classical markers (i.e., CD36, CD163, CD271, CD200, CD273, CD274, CD146, CD248, and CD140B) that may potentially discriminate AMSCs from other cell types. More importantly, these markers exhibit variability in cell surface expression among different cell isolates from a diverse cohort of donors, including freshly prepared, previously frozen, or proliferative state AMSCs and may be informative when manufacturing cells. Conclusions Our study establishes that clinical-grade AMSCs expanded in hPL represent a homogeneous cell culture population according to classical markers,. Additionally, we validated new biomarkers for further AMSC characterization that may provide novel information guiding the development of new release criteria. Clinical trials Use of Autologous Bone Marrow Aspirate Concentrate in Painful Knee Osteoarthritis (BMAC): Clinicaltrials.gov NCT01931007 . Registered August 26, 2013. MSC for Occlusive Disease of the Kidney: Clinicaltrials.gov NCT01840540 . Registered April 23, 2013. Mesenchymal Stem Cell Therapy in Multiple System Atrophy: Clinicaltrials.gov NCT02315027 . Registered October 31, 2014. Efficacy and Safety of Adult Human Mesenchymal Stem Cells to Treat Steroid Refractory Acute Graft Versus Host Disease. Clinicaltrials.gov NCT00366145 . Registered August 17, 2006. A Dose-escalation Safety Trial for Intrathecal Autologous Mesenchymal Stem Cell Therapy in Amyotrophic Lateral Sclerosis. Clinicaltrials.gov NCT01609283 . Registered May 18, 2012.

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate higher CTC counts in patients with late-stage prostate cancer ( n  = 13) compared to healthy controls ( n  = 8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate significantly higher CTC counts in patients with late-stage prostate cancer (n=13) compared to healthy controls (n=8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve the sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate significantly higher CTC counts in patients with late-stage prostate cancer (n=13) compared to healthy controls (n=8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.