scholarly journals Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

2021 ◽  
Vol 7 (3) ◽  
pp. eabe0431
Author(s):  
Shiyi Cheng ◽  
Sipei Fu ◽  
Yumi Mun Kim ◽  
Weiye Song ◽  
Yunzhe Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Structural Information ◽  
Large Cell ◽  
Label Free ◽  
Cell Cycles ◽  
Imaging Cytometry ◽  
Single Cell Profiling ◽  
The Rich ◽  
Reflectance Microscopy ◽  
Labeling Method

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning–augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell–level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

scholarly journals Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

2020 ◽  
Author(s):  
Shiyi Cheng ◽  
Sipei Fu ◽  
Yumi Mun Kim ◽  
Weiye Song ◽  
Yunzhe Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Structural Information ◽  
Large Cell ◽  
Label Free ◽  
Cell Cycles ◽  
Imaging Cytometry ◽  
Single Cell Profiling ◽  
The Rich ◽  
Reflectance Microscopy ◽  
Labeling Method

AbstractTraditional imaging cytometry uses fluorescence markers to identify specific structures, but is limited in throughput by the labeling process. Here we develop a label-free technique that alleviates the physical staining and provides highly multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts highly accurate subcellular features after training on immunofluorescence images. We demonstrate up to 3× improvement in the prediction accuracy over the state-of-the-art. Beyond fluorescence prediction, we demonstrate that single-cell level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis and DNA synthesis. We further show that the multiplexed readouts enable accurate multi-parametric single-cell profiling across a large cell population. Our method can dramatically improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

scholarly journals ‘Simultaneous tracking of cell motility in liquid and at the solid-liquid interface’

2019 ◽  
Author(s):  
Andrew L. Hook ◽  
James L. Flewellen ◽  
Irwin M. Zaid ◽  
Richard M. Berry ◽  
Jean-Frédéric Dubern ◽  
...  
Keyword(s):  
Single Cell ◽  
Liquid Interface ◽  
Bacterial Biofilm ◽  
Digital Holographic Microscopy ◽  
Label Free ◽  
Near Surface ◽  
Solid Liquid Interface ◽  
Reflectance Microscopy ◽  
Solid Liquid ◽  
Total Internal Reflectance

1.AbstractTo better understand key behaviors of living cells, such as bacterial biofilm formation, they must be observed above surfaces and at the interface between the surface and liquid medium. We have established a methodology for label-free imaging and tracking of individual cells simultaneously at both the solid-liquid interface and within the bulk, utilizing imaging modes of digital holographic microscopy (DHM) in 3D, differential interference contrast (DIC) and total internal reflectance microscopy (TIRM) in 2D as well as analysis protocols using a bespoke software package. We illustrate the power of this method by making detailed single cell measurements ofPseudomonas aeruginosain the first minutes of their interaction with a glass surface, focusing on the role of the flagella stators,motABandmotCD. Using this new method we have determined their relative contributions to bulk and near surface motion for populations of cells at the single cell level.

scholarly journals Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization

2021 ◽  
Author(s):  
Ana Rita Pombo Antunes ◽  
Isabelle Scheyltjens ◽  
Francesca Lodi ◽  
Julie Messiaen ◽  
Asier Antoranz ◽  
...  
Keyword(s):  
Single Cell ◽  
Myeloid Cells ◽  
Disease Stage ◽  
Single Cell Profiling

scholarly journals Designed improvement to T-cell immunotherapy by multidimensional single cell profiling

2021 ◽  
Vol 9 (3) ◽  
pp. e001877
Author(s):  
Irfan N Bandey ◽  
Jay R T Adolacion ◽  
Gabrielle Romain ◽  
Melisa Martinez Paniagua ◽  
Xingyue An ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Adoptive Cell Therapy ◽  
Population Heterogeneity ◽  
Serial Killer ◽  
Car T Cells ◽  
Car T ◽  
Single Cell Profiling ◽  
Killer T Cells

BackgroundAdoptive cell therapy based on the infusion of chimeric antigen receptor (CAR) T cells has shown remarkable efficacy for the treatment of hematologic malignancies. The primary mechanism of action of these infused T cells is the direct killing of tumor cells expressing the cognate antigen. However, understanding why only some T cells are capable of killing, and identifying mechanisms that can improve killing has remained elusive.MethodsTo identify molecular and cellular mechanisms that can improve T-cell killing, we utilized integrated high-throughput single-cell functional profiling by microscopy, followed by robotic retrieval and transcriptional profiling.ResultsWith the aid of mathematical modeling we demonstrate that non-killer CAR T cells comprise a heterogeneous population that arise from failure in each of the discrete steps leading to the killing. Differential transcriptional single-cell profiling of killers and non-killers identified CD137 as an inducible costimulatory molecule upregulated on killer T cells. Our single-cell profiling results directly demonstrate that inducible CD137 is feature of killer (and serial killer) T cells and this marks a different subset compared with the CD107apos (degranulating) subset of CAR T cells. Ligation of the induced CD137 with CD137 ligand (CD137L) leads to younger CD19 CAR T cells with sustained killing and lower exhaustion. We genetically modified CAR T cells to co-express CD137L, in trans, and this lead to a profound improvement in anti-tumor efficacy in leukemia and refractory ovarian cancer models in mice.ConclusionsBroadly, our results illustrate that while non-killer T cells are reflective of population heterogeneity, integrated single-cell profiling can enable identification of mechanisms that can enhance the function/proliferation of killer T cells leading to direct anti-tumor benefit.

scholarly journals Single-cell profiling identifies pre-existing CD19-negative subclones in a B-ALL patient with CD19-negative relapse after CAR-T therapy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tracy Rabilloud ◽  
Delphine Potier ◽  
Saran Pankaew ◽  
Mathis Nozais ◽  
Marie Loosveld ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
T Cell ◽  
Single Cell ◽  
Remission Rate ◽  
Lymphoblastic Leukemia ◽  
Leukemic Cells ◽  
T Cell Therapy ◽  
Car T Cell Therapy ◽  
Car T ◽  
Single Cell Profiling

AbstractChimeric antigen receptor T cell (CAR-T) targeting the CD19 antigen represents an innovative therapeutic approach to improve the outcome of relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL). Yet, despite a high initial remission rate, CAR-T therapy ultimately fails for some patients. Notably, around half of relapsing patients develop CD19 negative (CD19neg) B-ALL allowing leukemic cells to evade CD19-targeted therapy. Herein, we investigate leukemic cells of a relapsing B-ALL patient, at two-time points: before (T1) and after (T2) anti-CD19 CAR-T treatment. We show that at T2, the B-ALL relapse is CD19 negative due to the expression of a non-functional CD19 transcript retaining intron 2. Then, using single-cell RNA sequencing (scRNAseq) approach, we demonstrate that CD19neg leukemic cells were present before CAR-T cell therapy and thus that the relapse results from the selection of these rare CD19neg B-ALL clones. In conclusion, our study shows that scRNAseq profiling can reveal pre-existing CD19neg subclones, raising the possibility to assess the risk of targeted therapy failure.

scholarly journals Label-Free and Quantitative Dry Mass Monitoring for Single Cells during In Situ Culture

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1635
Author(s):  
Ya Su ◽  
Rongxin Fu ◽  
Wenli Du ◽  
Han Yang ◽  
Li Ma ◽  
...  
Keyword(s):  
Cell Growth ◽  
Single Cell ◽  
Hela Cells ◽  
Quantitative Measurement ◽  
Single Cells ◽  
Dry Mass ◽  
Quantitative Detection ◽  
Label Free ◽  
Mass Sensitivity

Quantitative measurement of single cells can provide in-depth information about cell morphology and metabolism. However, current live-cell imaging techniques have a lack of quantitative detection ability. Herein, we proposed a label-free and quantitative multichannel wide-field interferometric imaging (MWII) technique with femtogram dry mass sensitivity to monitor single-cell metabolism long-term in situ culture. We demonstrated that MWII could reveal the intrinsic status of cells despite fluctuating culture conditions with 3.48 nm optical path difference sensitivity, 0.97 fg dry mass sensitivity and 2.4% average maximum relative change (maximum change/average) in dry mass. Utilizing the MWII system, different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before the M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. Furthermore, HeLa cells were treated with Gemcitabine to reveal the relationship between single-cell heterogeneity and chemotherapeutic efficacy. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. In conclusion, MWII was presented as a technique for single-cell dry mass quantitative measurement, which had significant potential applications for cell growth dynamics research, cell subtype analysis, cell health characterization, medication guidance and adjuvant drug development.

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