  Spatial transcriptomics (ST) enables interrogating the molecular composition
of tissue with ever-increasing resolution, depth, and sensitivity. However,
costs, rapidly evolving technology, and lack of standards have constrained
computational methods in ST to narrow tasks and small cohorts. In addition, the
underlying tissue morphology as reflected by H&E-stained whole slide images
(WSIs) encodes rich information often overlooked in ST studies. Here, we
introduce HEST-1k, a collection of 1,108 spatial transcriptomic profiles, each
linked to a WSI and metadata. HEST-1k was assembled using HEST-Library from 131
public and internal cohorts encompassing 25 organs, two species (Homo Sapiens
and Mus Musculus), and 320 cancer samples from 25 cancer types. HEST-1k
processing enabled the identification of 1.5 million expression--morphology
pairs and 60 million nuclei. HEST-1k is tested on three use cases: (1)
benchmarking foundation models for histopathology (HEST-Benchmark), (2)
biomarker identification, and (3) multimodal representation learning. HEST-1k,
HEST-Library, and HEST-Benchmark can be freely accessed via
https://github.com/mahmoodlab/hest.
