Statistical analysis of scanning fluorescence correlation spectroscopy data differentiates free from hindered diffusion

Cells rely on versatile diffusion dynamics in their plasma membrane. Quantification of this often heterogeneous diffusion is essential to the understanding of cell regulation and function. Yet such measurements remain a major challenge in cell biology, usually due to low sampling throughput, a necessity for dedicated equipment, sophisticated fluorescent label strategies, and limited sensitivity. Here, we introduce a robust, broadly-applicable statistical analysis pipeline for large scanning-fluorescence-correlation-spectroscopy data sets, which uncovers the nanoscale heterogeneity of the plasma membrane in living cells by differentiating free from hindered diffusion modes of fluorescent lipid and protein analogues

Statistical analysis of scanning fluorescence correlation spectroscopy data differentiates free from hindered diffusion

Cells rely on versatile diffusion dynamics in their plasma membrane. Quantification of this often heterogeneous diffusion is essential to the understanding of cell regulation and function. Yet such measurements remain a major challenge in cell biology, usually due to low sampling throughput, a necessity for dedicated equipment, sophisticated fluorescent label strategies, and limited sensitivity. Here, we introduce a robust, broadly-applicable statistical analysis pipeline for large scanning-fluorescence-correlation-spectroscopy data sets, which uncovers the nanoscale heterogeneity of the plasma membrane in living cells by differentiating free from hindered diffusion modes of fluorescent lipid and protein analogues

Super-Resolved Traction Force Microscopy (STFM).

Measuring small forces is a major challenge in cell biology. Here we improve the spatial resolution and accuracy of force reconstruction of the well-established technique of traction force microscopy (TFM) using STED microscopy. The increased spatial resolution of STED-TFM (STFM) allows a greater than 5-fold higher sampling of the forces generated by the cell than conventional TFM, accessing the nano instead of the micron scale. This improvement is highlighted by computer simulations and an activating RBL cell model system

CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues

Super-resolved traction force microscopy (STFM)

Measuring small forces is a major challenge in cell biology. Here we improve the spatial resolution and accuracy of force reconstruction of the well-established technique of traction force microscopy (TFM) using STED microscopy. The increased spatial resolution of STED-TFM (STFM) allows a greater than 5-fold higher sampling of the forces generated by the cell than conventional TFM, accessing the nano instead of the micron scale. This improvement is highlighted by computer simulations and an activating RBL cell model system

Super-resolution microscopy reveals compartmentalization of peroxisomal membrane proteins

Membrane-associated events during peroxisomal protein import processes play an essential role in peroxisome functionality. Many details of these processes are not known due to missing spatial resolution of technologies capable of investigating peroxisomes directly in the cell. Here, we present the use of super-resolution optical stimulated emission depletion microscopy to investigate with sub-60-nm resolution the heterogeneous spatial organization of the peroxisomal proteins PEX5, PEX14, and PEX11 around actively importing peroxisomes, showing distinct differences between these peroxins. Moreover, imported protein sterol carrier protein 2 (SCP2) occupies only a subregion of larger peroxisomes, highlighting the heterogeneous distribution of proteins even within the peroxisome. Finally, our data reveal subpopulations of peroxisomes showing only weak colocalization between PEX14 and PEX5 or PEX11 but at the same time a clear compartmentalized organization. This compartmentalization, which was less evident in cases of strong colocalization, indicates dynamic protein reorganization linked to changes occurring in the peroxisomes. Through the use of multicolor stimulated emission depletion microscopy, we have been able to characterize peroxisomes and their constituents to a yet unseen level of detail while maintaining a highly statistical approach, paving the way for equally complex biological studies in the future

CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues