Biology subjects

Collman, F.

Publications and source records attributed to Collman, F..

21 records · Page 2Linked to original sources

bioRxiv2018

MosaicPlanner: Hardware Agnostic Array Tomography Acquisition Software

Array tomography (AT) is a technique for acquiring high resolution highly multiplexed imagery from series of ultra-thin sections arranged as an array on a rigid substrate. Specialized microscope control has been required to utilize AT as an imaging technique, which is often time consuming, and yields small volume data sets. Here we present MosaicPlanner, an open source software platform for light level AT, that streamlines the acquisition process and utilizes the general microscope control API provided by Micro-Manager, allowing AT data to be acquired on a wide variety of microscope hardware. This report provides a description of the MosaicPlanner software design, and platform improvements that were implemented to increase the acquisition speed of high volume, multiplexed AT datasets.

neuroscience↗

bioRxiv2018

Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.

cell biology↗

bioRxiv2017

Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction

Fluorescence microscopy has enabled imaging of key subcellular structures in living cells; however, the use of fluorescent dyes and proteins is often expensive, time-consuming, and damaging to cells. Here, we present a tool for the prediction of fluorescently labeled structures in live cells solely from 3D brightfield microscopy images. We show the utility of this approach in predicting several structures of interest from the same static 3D brightfield image, and show that the same tool can prospectively be used to predict the spatiotemporal position of these structures from a bright-field time series. This approach could also be useful in a variety of application areas, such as cross-modal image registration, quantification of live cell imaging, and determination of cell state changes.

cell biology↗