High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging

He, Shikun; Bhatt, Ruchir; Brown, Carl; Brown, Emily; Buhr, Derek L.; Chantranuvatana, Kan; Danaher, Patrick; Dunaway, Dwayne; Garrison, Ryan G.; Geiss, Gary; Gregory, Mark; Hoang, Margaret L.; Khafizov, Rustem; Killingbeck, Emily; Kim, Dae; Kim, Tae Kyung; Kim, Youngmi; Klock, Andrew; Korukonda, Mithra; Kutchma, Alecksandr; Lewis, Zachary; Liang, Yan; Nelson, Jeffrey S.; Ong, Giang T.; Perillo, Evan P.; Phan, Joseph C.; Phan-Everson, Tien; Piazza, Erin; Rane, Tushar D.; Reitz, Zachary; Rhodes, M. J. C.; Rosenbloom, Alyssa B.; Ross, David; Sato, Hiromi; Wardhani, Aster; Williams-Wietzikoski, Corey A.; Wu, Lidan; Beechem, Joseph M.

doi:10.1038/s41587-022-01483-z

Cited by 323 publications

(276 citation statements)

References 65 publications

Supporting

Mentioning

276

Contrasting

Order By: Relevance

“…The NanoString CosMx FFPE non-small cell lung cancer public data release reports 960-plex RNA data from 771,236 cells across 5 tumors. We applied Insitutype’s semi-supervised approach to this dataset, calling previously defined immune and stroma cell types (Danaher 2022) while detecting additional cell types missing from the reference profiles.…”

Section: Resultsmentioning

confidence: 99%

“…Spatial transcriptomics platforms include negative control “genes” designed to assess background rates. Some platforms (He 2022) use physical negative controls - hybridization probes targeting sequences not present in any known genome (External RNA controls consortium 2005). Other platforms (Wang 2018) use computational negative controls, directing their software to detect barcodes not generated by any gene.…”

Section: Methodsmentioning

confidence: 99%

“…Cells could be binned based on their x,y positions across a tissue. Or more informatively, a spatial clustering algorithm (Zhu 2018, Chidester 2022, Li 2022, Singhal 2022) could be used to cluster cells based on the gene expression profile in a small region around them, linking cells from the same spatial context wherever they fall within the tissue. To facilitate derivation of the cohort vector, the Insitutype library provides a function for automatically dividing cells into cohorts based on their immunofluorescence intensities and the gene expression profile of their spatial neighborhood.…”

Section: Methodsmentioning

confidence: 99%

“…Spatial transcriptomics platforms detect individual RNA molecules where they lie on tissue slides, producing datasets of single-cell gene expression data while preserving cells’ location data (He 2022, Wang 2018). A typical experiment will measure 50-1000 genes and 10 5 to 10 7 cells (Anderson 2022).…”

Section: Introductionmentioning

confidence: 99%

“…One class harnesses cells’ spatial locations to improve cell typing accuracy. Some of these methods model the frequency of each cell type within cells’ neighborhoods (Teng 2022), or within spatial domains (Chidester 2022, Li 2022). Others merge neighborhood information with the spatial data, appending neighborhood expression to the single-cell expression matrix prior to clustering (Singhal 2022) or by considering spatial information in the construction of a distance matrix between cells (Avesani 2022).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Danaher¹,

Zhao²,

Yang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Accurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data. Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cell's expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Danaher¹,

Zhao²,

Yang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function

Rocca,

Galli,

Celant

et al. 2024

FEBS Letters

View full text Add to dashboard Cite

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single‐cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross‐talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue‐specific functions and aiding newcomers in navigating this field.

show abstract

Analytical Tools to Quantitate Immune Mediated Effects

Pavelko,

Villasboas

2024

Precision Cancer Therapies Vol 2 ‐ Immunologic Approaches for the Treatment of Lymphoid Malignancies ‐ From Concept to Practice

View full text Add to dashboard Cite

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging

Cited by 323 publications

References 65 publications

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function

Analytical Tools to Quantitate Immune Mediated Effects

Contact Info

Product

Resources

About