Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity

McKinley, Eliot T.; Sui, Yunxia; Al-Kofahi, Yousef; Millis, Bryan A.; Tyska, Matthew J.; Roland, Joseph T.; Santamaría-Pang, Alberto; Ohland, Christina; Jobin, Christian; Franklin, Jeffrey L.; Lau, Ken S.; Gerdes, Michael J.; Coffey, Robert J.

doi:10.1172/jci.insight.93487

Cited by 120 publications

(141 citation statements)

References 54 publications

Supporting

Mentioning

134

Contrasting

Unclassified

Order By: Relevance

“…Both our computational and experimental analyses indicate an Atoh1-independent, and possibly, non-secretory cell origin of tuft cells in the small intestine, and an alternative origin of tuft cells in the colon. These observations support recent speculations by Gerbe and Jay regarding the potential functional differences among tuft cells at different anatomical sites (Gerbe and Jay, 2016), as well as our previous observations of different tuft cell distributions between the small intestine and the colon (McKinley et al, 2017). The discrepancies in phenotypes among studies and organ systems may arise due to the secondary effects of the microbiome.…”

Section: Discussionsupporting

confidence: 92%

“…A full depiction of all markers throughout the crypt-luminal axis of the small intestine and colon is presented in Figures S8 and S9, respectively. Object segmentation with a super-membrane mask (β-Catenin, NaKATPase, PCK26, CD44v6), preprocessing to remove non-cells, and quantification of single cells were performed as previously described (McKinley et al, 2017). We also applied an additional filter to remove data points (cells) that were gated negative for all markers and, therefore, uninformative to the analysis.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast to previous findings (Gerbe et al, 2011), tuft cells, as marked by DCLK1, increased in the small intestine, rather than being suppressed (Figures 5A–B, 5E, S15A–C). These DCLK1 cells are bona fide tuft cells and not stem-like cells, as evidenced by their villus localization, candle-like “tufted” morphologies, and multi-marker protein signature (McKinley et al, 2017) (Figures 5B, 5E, S15B). …”

Section: Resultsmentioning

confidence: 99%

“…The discrepancies in phenotypes among studies and organ systems may arise due to the secondary effects of the microbiome. It has been shown that tuft cells can be regulated by luminal parasites, such as helminths (Gerbe et al, 2016; Howitt et al, 2016; von Moltke et al, 2015), and commensal bacteria (McKinley et al, 2017). As such, knockout of Atoh1 ablates microbiome-regulating goblet and Paneth cells, which can subsequently affect tuft cells as a secondary effect.…”

Section: Discussionmentioning

confidence: 99%

“…For computational analyses, N refers to the number of resampled runs on the same data consisting of hundreds to thousands of cells. ANOVA and t -test were used based on previous data to quantify that the tuft cell distribution in the gut follows a Gaussian distribution over multiple samples (McKinley et al, 2017). …”

Section: Star Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

et al. 2018

Self Cite

View full text Add to dashboard Cite

Summary Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories. p-Creode is publicly available for download here: https://github.com/KenLauLab/pCreode.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Star Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

Dynamic tuft cell expansion during gastric metaplasia and dysplasia

Jang,

Kim,

Lee

et al. 2023

The Journal of Pathology CR

View full text Add to dashboard Cite

Tuft cells are chemosensory cells associated with luminal homeostasis, immune response, and tumorigenesis in the gastrointestinal tract. We aimed to elucidate alterations in tuft cell populations during gastric atrophy and tumorigenesis in humans with correlative comparison to relevant mouse models. Tuft cell distribution was determined in human stomachs from organ donors and in gastric pathologies including Ménétrier's disease, Helicobacter pylori gastritis, intestinal metaplasia (IM), and gastric tumors. Tuft cell populations were examined in Lrig1‐KrasG12D, Mist1‐KrasG12D, and MT‐TGFα mice. Tuft cells were evenly distributed throughout the entire normal human stomach, primarily concentrated in the isthmal region in the fundus. Ménétrier's disease stomach showed increased tuft cells. Similarly, Lrig1‐Kras mice and mice overexpressing TGFα showed marked foveolar hyperplasia and expanded tuft cell populations. Human stomach with IM or dysplasia also showed increased tuft cell numbers. Similarly, Mist1‐Kras mice had increased numbers of tuft cells during metaplasia and dysplasia development. In human gastric cancers, tuft cells were rarely observed, but showed positive associations with well‐differentiated lesions. In mouse gastric cancer xenografts, tuft cells were restricted to dysplastic well‐differentiated mucinous cysts and were lost in less differentiated cancers. Taken together, tuft cell populations increased in atrophic human gastric pathologies, metaplasia, and dysplasia, but were decreased in gastric cancers. Similar findings were observed in mouse models, suggesting that, while tuft cells are associated with precancerous pathologies, their loss is most associated with the progression to invasive cancer.

show abstract

A Simple Method for Creating a High‐Content Microscope for Imaging Multiplexed Tissue Microarrays

Abtahi

Gliksman²,

Heneghan

et al. 2021

Current Protocols

View full text Add to dashboard Cite

High-throughput, high-content imaging technologies and multiplex slide scanning have become widely used. Advantages of these approaches include the ability to archive digital copies of slides, review slides as teams using virtual microscopy software, and standardize analytical approaches. The cost and hardware and software inflexibility of dedicated slide scanning devices can, however, complicate implementation. Here, we describe a simple method that allows any microscope to be used for slide scanning. The only requirements are that the microscope be equipped with a motorized filter turret or wheels (for multi-channel fluorescence) and a motorized xyz stage. This example uses MetaMorph software, but the same principles can be used with any microscope control software that includes a few standard functions and allows programming of simple command routines, or journals. The series of journals that implement the method perform key functions, including assistance in defining an unlimited number of regions of interest (ROI) and imaging parameters. Fully automated acquisition is rapid, taking less than 3 hr to image fifty 2.5-mm ROIs in four channels. Following acquisition, images can be easily stitched and displayed using open-source or commercial image-processing and virtual microscope applications.

show abstract

Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity

Cited by 120 publications

References 54 publications

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

Dynamic tuft cell expansion during gastric metaplasia and dysplasia

A Simple Method for Creating a High‐Content Microscope for Imaging Multiplexed Tissue Microarrays

Contact Info

Product

Resources

About