| Isolation, cultivation, and characterization of human mesenchymal stem cells |
50 |
| Deep Learning in Image Cytometry: A Review |
30 |
| High-Parameter Mass Cytometry Evaluation of Relapsed/Refractory Multiple Myeloma Patients Treated with Daratumumab Demonstrates Immune Modulation as a Novel Mechanism of Action |
26 |
| Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images |
22 |
| Mammalian MSC from selected species: Features and applications |
19 |
| NADH Autofluorescence-A Marker on its Way to Boost Bioenergetic Research |
15 |
| The Parsortix (TM) Cell Separation System-A versatile liquid biopsy platform |
15 |
| Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping |
13 |
| The anatomy of single cell mass cytometry data |
13 |
| OMIP-051-28-color flow cytometry panel to characterize B cells and myeloid cells |
13 |
| The fibroblast surface markers FAP, anti-fibroblast, and FSP are expressed by cells of epithelial origin and may be altered during epithelial-to-mesenchymal transition |
12 |
| Microfluidic Based Optical Microscopes on Chip |
11 |
| Best Practices for Preparing a Single Cell Suspension from Solid Tissues for Flow Cytometry |
11 |
| Comparison of JC-1 and MitoTracker probes for mitochondrial viability assessment in stored canine platelet concentrates: A flow cytometry study |
11 |
| Autofluorescence lifetime imaging of cellular metabolism: Sensitivity toward cell density, pH, intracellular, and intercellular heterogeneity |
10 |
| Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification |
9 |
| Stabilizing Antibody Cocktails for Mass Cytometry |
9 |
| The metabolic syndrome alters the miRNA signature of porcine adipose tissue-derived mesenchymal stem cells |
9 |
| Single-cell redox states analyzed by fluorescence lifetime metrics and tryptophan FRET interaction with NAD(P)H |
9 |
| Centrifugation affects the purity of liquid biopsy-based tumor biomarkers |
9 |
| Cyt-Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2018 Conference Workshops |
9 |
| Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding |
9 |
| OMIP-058: 30-Parameter Flow Cytometry Panel to Characterize iNKT, NK, Unconventional and Conventional T Cells |
8 |
| How to Agree on a CTC: Evaluating the Consensus in Circulating Tumor Cell Scoring |
8 |
| Diagnostic leukapheresis for CTC analysis in breast cancer patients: CTC frequency, clinical experiences and recommendations for standardized reporting |
8 |
| Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning |
8 |
| ClearCell (R) FX, a label-free microfluidics technology for enrichment of viable circulating tumor cells |
8 |
| OMIP-050: A 28-color/30-parameter Fluorescence Flow Cytometry Panel to Enumerate and Characterize Cells Expressing a Wide Array of Immune Checkpoint Molecules |
8 |
| Comprehensive Phenotyping of T Cells Using Flow Cytometry |
8 |
| Label-Free Identification of White Blood Cells Using Machine Learning |
8 |
| OMIP-060: 30-Parameter Flow Cytometry Panel to Assess T Cell Effector Functions and Regulatory T Cells |
7 |
| Enhancing Type I Photochemistry in Photodynamic Therapy Under Near Infrared Light by Using Antennae-Fullerene Complexes |
7 |
| VyCAP's puncher technology for single cell identification, isolation, and analysis |
7 |
| DEPArray (TM) system: An automatic image-based sorter for isolation of pure circulating tumor cells |
7 |
| Untangling cell tracks: Quantifying cell migration by time lapse image data analysis |
7 |
| Deep phenotyping of immune cell populations by optimized and standardized flow cytometry analyses |
7 |
| OMIP-042: 21-color flow cytometry to comprehensively immunophenotype major lymphocyte and myeloid subsets in human peripheral blood |
7 |
| Flow cytometric fingerprinting for microbial strain discrimination and physiological characterization |
7 |
| Standardization of Flow Cytometric Immunophenotyping for Hematological Malignancies: The FranceFlow Group Experience |
7 |
| Conserved and variable: Understanding mammary stem cells across species |
6 |
| Automation of the in vitro micronucleus assay using the Imagestream((R)) imaging flow cytometer |
6 |
| Improving Quality, Reproducibility, and Usability of FRET-Based Tension Sensors |
6 |
| Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning |
6 |
| DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data |
6 |
| Unique Calibrators Derived from Fluorescence-Activated Nanoparticle Sorting for Flow Cytometric Size Estimation of Artificial Vesicles: Possibilities and Limitations |
6 |
| Spectral imaging of FRET-based sensors reveals sustained cAMP gradients in three spatial dimensions |
5 |
| Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps |
5 |
| Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines |
5 |
| Proteomic Profiling of Native Unpassaged and Culture-Expanded Mesenchymal Stromal Cells (MSC) |
5 |
| Cyt-Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2019 Conference Workshops |
5 |
