Nathan Heath Patterson

Unmixing of Imaging Mass Spectrometry Measurements Using Microscopy-Informed Constraints

This paper introduces TULIP, a method to unmix IMS data using microscopy constraints, improving analysis of tissue structures in synthetic and kidney datasets.

Integrating Ambient Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to Identify Gene-Metabolite Correlations

The paper integrates spatial metabolomics and spatial transcriptomics on cancer tissues to identify gene-metabolite correlations.

Revealing In Situ Molecular Profiles of Glomerular Cell Types and Substructures with Integrated Imaging Mass Spectrometry and Multiplexed Immunofluorescence Microscopy

This paper integrates MALDI IMS and MxIF microscopy to analyze lipid and molecular profiles of glomerular cell types and substructures in human kidney tissue.

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-DBiT co-sequences spatial transcriptomes and gRNAs on one tissue section, supporting genome-wide in vivo CRISPR screens in FFPE and fresh-frozen tumors across three cancer models.

Addressing persistent challenges in digital image analysis of cancerous tissues

The paper discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

Robust and generalizable segmentation of human functional tissue units

The paper evaluates five algorithms for segmenting functional tissue units in kidney and colon tissues using histological stains, comparing their accuracy and generalizability.

Tissue Registration and Exploration User Interfaces in support of a Human Reference Atlas

The paper presents software tools and user interfaces for annotating and exploring tissue data in a human reference atlas, enabling data harmonization and public access.

Automated Segmentation of Kidney Nephron Structures by Deep Learning Models on Label-free Autofluorescence Microscopy for Spatial Multi-omics Data Acquisition and Mining

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

This paper introduces an automated workflow using SHAP to identify biomarker candidates in imaging mass spectrometry data by ranking molecular species based on their predictive importance for biological class recognition.