Automated classification of cellular expression in multiplexed imaging data with Nimbus.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope using the Tile/SED/Array Interface.

Multiplexed ion beam imaging (MIBI) is a next-generation mass spectrometry-based microscopy technique that generates 40+ plex images of protein expression in histologic tissues, enabling detailed dissection of cellular phenotypes and histoarchitectural organization. A key bottleneck in operation occurs when users select the physical locations on the tissue for imaging. As the scale and complexity of MIBI experiments have increased, the manufacturer-provided interface and third-party tools have become increasingly unwieldy for imaging large tissue microarrays and tiled tissue areas. Thus, a web-based, interactive, what-you-see-is-what-you-get (WYSIWYG) graphical interface layer - the tile/SED/array Interface (TSAI) - was developed for users to set imaging locations using familiar and intuitive mouse gestures such as drag-and-drop, click-and-drag, and polygon drawing. Written according to web standards already built into modern web browsers, it requires no installation of external programs, extensions, or compilers. Of interest to the hundreds of current MIBI users, this interface dramatically simplifies and accelerates the setup of large, complex MIBI runs.