Neuro-Informatics seminars

Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

The standard method for staining structures in the brain is to slice the brain into 2D sections. Each slice is treated using a technique such as in-situ hybridization to examine the spatial expression of a particular molecule at a given developmental timepoint. Depending on the brain structures being studied, slices can be made coronally, sagitally, or at any angle that is thought to be optimal for analysis. However, assimilating the information presented in the 2D slice images to gain quantitiative and informative 3D expression patterns is challenging. Even if expression levels are presented as voxels, to give 3D expression clouds, it can be difficult to compare expression across individuals and analysing such data requires significant expertise and imagination. In this talk, I will describe a new approach to examining histology slices, in which the user defines the brain structure of interest by drawing curves around it on each slice in a set and the depth of tissue from which to sample expression. The sampled 'curves' are then assembled into a 3D surface, which can then be transformed onto a common reference frame for comparative analysis. I will show how other neuroscientists can obtain and use the tool, which is called Stalefish, to analyse their own image data with no (or minimal) changes to their slice preparation workflow.

The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence are often inefficient and the results hard to interpret. Here we introduce Suite2p: a fast, accurate, parameter-free and complete pipeline that registers raw movies, detects active and/or inactive cells (using Cellpose), extracts their calcium traces and infers their spike times. Suite2p runs faster than real time on standard workstations and outperforms state-of-the-art methods on newly developed ground-truth benchmarks for motion correction and cell detection.