# Principal subspace analysis of cryo-EM heterogeneity

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $343,461

## Abstract

Biological macromolecules can be imaged by single-particle cryogenic electron-microscopy (cryo-EM) without
crystallization. This offers the possibility of imaging heterogeneous samples – samples in which the
macromolecule is in different conformational states. This research proposes a new method for analyzing such
heterogeneous samples. Based on a Fourier-slice theorem for covariance functions, heterogeneity is analyzed
by directly reconstructing 3d principal components and estimating the population densities of different
conformations in the principal subspace, which is the subspace spanned by the principal components. This
new methodology offers many advantages over the popular method of 3d classification: the new method works
for continuous and discrete conformational states, it can organize conformational states in a meaningful way,
and it can separate sample imperfections from conformational states. This research proposes to fully develop
this methodology, validate it with simulations and real cryo-EM images, and release an open source software
package for the use by the cryo-EM community.

## Key facts

- **NIH application ID:** 9870938
- **Project number:** 5R01GM125769-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Hemant D Tagare
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $343,461
- **Award type:** 5
- **Project period:** 2018-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9870938

## Citation

> US National Institutes of Health, RePORTER application 9870938, Principal subspace analysis of cryo-EM heterogeneity (5R01GM125769-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9870938. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*