Automated quantitative analysis of multiple cardiomyocytes at the single-cell level with three-dimensional holographic imaging informatics

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

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No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Biomedical Optics Express ◽

10.1364/boe.10.004276 ◽

2019 ◽

Vol 10 (8) ◽

pp. 4276 ◽

Cited By ~ 7

Author(s):

Keyvan Jaferzadeh ◽

Seung-Hyeon Hwang ◽

Inkyu Moon ◽

Bahram Javidi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Single Cell ◽

Deep Convolutional Neural Network ◽

Single Cell Level ◽

Cell Level ◽

Holographic Imaging

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Quantitative analysis “at the single cell level” of the novel CD28-CD11b-subpopulation of CD8+ T lymphocytes

Journal of Dermatological Science ◽

10.1016/s0923-1811(98)83564-0 ◽

1998 ◽

Vol 16 ◽

pp. S95 ◽

Cited By ~ 3

Author(s):

A. Lonati ◽

S. Licenziati ◽

M. Marcelli ◽

D. Canaris ◽

G. Pasolini ◽

...

Keyword(s):

Quantitative Analysis ◽

T Lymphocytes ◽

Single Cell ◽

Single Cell Level ◽

The Novel ◽

Cell Level ◽

Cd8 T Lymphocytes

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2P323 Quantitative Analysis of Expressed Rare Messages in Single Cell Level using Gold Nano-Particles

Seibutsu Butsuri ◽

10.2142/biophys.45.s200_3 ◽

2005 ◽

Vol 45 (supplement) ◽

pp. S200

Author(s):

H. Kim ◽

A. Kira ◽

H. Kohno ◽

K. Matsumura ◽

K. Orita ◽

...

Keyword(s):

Quantitative Analysis ◽

Single Cell ◽

Nano Particles ◽

Single Cell Level ◽

Cell Level ◽

Gold Nano Particles

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Heterogeneity among human mononuclear phagocytes in their secretion of lysozyme, interleukin 1 and type-β transforming growth factor: a quantitative analysis at the single-cell level

European Journal of Immunology ◽

10.1002/eji.1830191111 ◽

1989 ◽

Vol 19 (11) ◽

pp. 2037-2043 ◽

Cited By ~ 20

Author(s):

Claire E. Lewis ◽

Sean P. Mccarthy ◽

Johann Lorenzen ◽

James O' D. Mcgee

Keyword(s):

Quantitative Analysis ◽

Growth Factor ◽

Single Cell ◽

Transforming Growth Factor ◽

Interleukin 1 ◽

Mononuclear Phagocytes ◽

Single Cell Level ◽

Cell Level

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Measurement of cytokine release by human cells A quantitative analysis at the single cell level using the reverse haemolytic plaque assay

Journal of Immunological Methods ◽

10.1016/0022-1759(90)90340-2 ◽

1990 ◽

Vol 127 (1) ◽

pp. 51-59 ◽

Cited By ~ 11

Author(s):

Claire E. Lewis ◽

Sean P. McCarthy ◽

Polly S. Richards ◽

Johann Lorenzen ◽

Elizabeth Horak ◽

...

Keyword(s):

Quantitative Analysis ◽

Single Cell ◽

Plaque Assay ◽

Human Cells ◽

Single Cell Level ◽

Cytokine Release ◽

Cell Level ◽

Reverse Haemolytic Plaque Assay

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FluidFM for single-cell biophysics

Nano Research ◽

10.1007/s12274-021-3573-y ◽

2021 ◽

Author(s):

Mi Li ◽

Lianqing Liu ◽

Tomaso Zambelli

Keyword(s):

Single Cell ◽

Three Dimensional ◽

Cell Biophysics ◽

Single Cell Level ◽

Cell Level ◽

Force Microscopy ◽

Atomic Force ◽

Underlying Mechanisms ◽

Aqueous Conditions ◽

Cell Force

AbstractFluidic force microscopy (FluidFM), which combines atomic force microscopy (AFM) with microchanneled cantilevers connected to a pressure controller, is a technique allowing the realization of force-sensitive nanopipette under aqueous conditions. FluidFM has unique advantages in simultaneous three-dimensional manipulations and mechanical measurements of biological specimens at the micro-/nanoscale. Over the past decade, FluidFM has shown its potential in biophysical assays particularly in the investigations at single-cell level, offering novel possibilities for discovering the underlying mechanisms guiding life activities. Here, we review the utilization of FluidFM to address biomechanical and biophysical issues in the life sciences. Firstly, the fundamentals of FluidFM are represented. Subsequently, the applications of FluidFM for biophysics at single-cell level are surveyed from several facets, including single-cell manipulations, single-cell force spectroscopy, and single-cell electrophysiology. Finally, the challenges and perspectives for future progressions are provided.

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