publications

2025

1. CellRepMethods

   Using spatial proteomics to enhance cell type assignments in histology images

   Monica T Dayao, Aaron T Mayer, Alexandro E Trevino, and 1 more author

   Cell Reports Methods, 2025

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   Hematoxylin and eosin (H&E) staining has been a standard in clinical histopathology for many decades but lacks molecular detail. Advances in multiplexed spatial proteomics imaging allow cell types and tissues to be annotated by their expression patterns as well as their morphological features. However, these technologies are at present unavailable in most clinical settings. In this work, we present a machine learning framework that leverages histopathology foundation models and paired H&E and spatial proteomic imaging data to enable enhanced cell type annotation on H&E-only datasets. We trained and evaluated our method on kidney datasets with paired H&E and spatial proteomic imaging data and found that models trained using our methods outperform models trained directly on the imaging data. We also show how our framework can be used to study biological differences between two major kidney diseases.

2023

1. Bioinformatics

   Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis

   Monica T Dayao, Alexandro Trevino, Honesty Kim, and 6 more authors

   Bioinformatics, Jun 2023

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   Motivation: Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. Results: Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. Availability and implementation: The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.

   @article{dayao2023deriving,
     title = {Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis},
     author = {Dayao, Monica T and Trevino, Alexandro and Kim, Honesty and Ruffalo, Matthew and D’Angio, H Blaize and Preska, Ryan and Duvvuri, Umamaheswar and Mayer, Aaron T and Bar-Joseph, Ziv},
     journal = {Bioinformatics},
     volume = {39},
     number = {Supplement\_1},
     pages = {i140--i148},
     month = jun,
     year = {2023},
     doi = {https://doi.org/10.1093/bioinformatics/btad245},
     publisher = {Oxford University Press},
   }

2022

1. NatureComm

   Membrane marker selection for segmenting single cell spatial proteomics data

   Monica T. Dayao, Maigan Brusko, Clive Wasserfall, and 1 more author

   Nature Communications, Apr 2022

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   The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells.

   @article{dayao2022membrane,
     title = {Membrane marker selection for segmenting single cell spatial proteomics data},
     author = {Dayao, Monica T. and Brusko, Maigan and Wasserfall, Clive and Bar-Joseph, Ziv},
     journal = {Nature Communications},
     volume = {13},
     issue = {1999},
     month = apr,
     year = {2022},
     doi = {https://doi.org/10.1038/s41467-022-29667-w},
     publisher = {Nature Publishing Group},
   }