An adaptable chromosome preparation methodology for use in invertebrate research organisms

Abstract Background The ability to efficiently visualize and manipulate chromosomes is fundamental to understanding the genome architecture of organisms. Conventional chromosome preparation protocols developed for mammalian cells and those relying on species-specific conditions are not suitable for many invertebrates. Hence, a simple and inexpensive chromosome preparation protocol, adaptable to multiple invertebrate species, is needed. Results We optimized a chromosome preparation protocol and applied it to several planarian species (phylum Platyhelminthes), the freshwater apple snail Pomacea canaliculata (phylum Mollusca), and the starlet sea anemone Nematostella vectensis (phylum Cnidaria). We demonstrated that both mitotically active adult tissues and embryos can be used as sources of metaphase chromosomes, expanding the potential use of this technique to invertebrates lacking cell lines and/or with limited access to the complete life cycle. Simple hypotonic treatment with deionized water was sufficient for karyotyping; growing cells in culture was not necessary. The obtained karyotypes allowed the identification of differences in ploidy and chromosome architecture among otherwise morphologically indistinguishable organisms, as in the case of a mixed population of planarians collected in the wild. Furthermore, we showed that in all tested organisms representing three different phyla this protocol could be effectively coupled with downstream applications, such as chromosome fluorescent in situ hybridization. Conclusions Our simple and inexpensive chromosome preparation protocol can be readily adapted to new invertebrate research organisms to accelerate the discovery of novel genomic patterns across the branches of the tree of life

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach.

Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett's esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3+ T regulatory cells and a CD163+ myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163+ myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R+CD1C+ myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

<p>Data supporting the findings of "Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer" manuscript. Files include patient/region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p&gt

Supplementary Figure S4 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S4. A. Elbow plot showing optimal number of RCNs (k=7) for grouping cellular neighborhoods. B. Bar chart showing the number of cells assigned to each of the seven RCNs across all αCD40 IA regions. C. Bar chart showing the percentage (out of 100) of cells assigned to each of the seven RCNs across all αCD40 IA regions. D. Stacked bar chart showing fraction (out of 1.0) of RCNs present per αCD40 IA region. E. Stacked bar chart showing average proportion (out of 1.0) of RCNs present in IA regions for eachαCD40-treated patient. F. Scatterplot reconstructions for each αCD40 IA region. Each dot represents a cell present in the IA, and each cell is colored by its original cell state phenotype (top scatterplot) or RCN assignment (bottom scatterplot).</p

Supplementary Figure S3 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S3. A. SHAP plot showing the top 30 features driving the IA model. Features are ordered on the y-axis such that those with a larger impact on the model’s predictions appear at the top of the SHAP plot. SHAP values are shown on the x-axis, with a value of zero (center) indicating no impact on the model, and negative or positive SHAP values predicting long DFS or short DFS, respectively. Red or blue dots indicate presence or absence, respectively, of the corresponding feature in tissues. B. Box plot showing feature values for each of the top 15 features for the model derived from IA regions of the αCD40 cohort split by DFS group (n = 30 regions from short DFS patients per feature; n = 13 regions from long DFS patients per feature). Each dot represents the log10+1 normalized feature value for one tissue region, which was inputted into the classifier model. Boxes = Q1 to Q3; whiskers = smallest and largest datapoints within 1.5*IQR +/- Q3/Q1; solid line = median. Mann–Whitney U-test used to determine statistical significance. P-values corrected using the Benjamini–Hochberg procedure. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.</p

Supplementary Figure S2 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S2. A. SHAP plots showing the top 30 features driving each histopathologic model. Features are ordered on the y-axis such that those with a larger impact on model’s predictions appear at the top of the SHAP plots. SHAP values are shown on the x-axis, with a value of zero (center) indicating no impact on the model, and negative or positive SHAP values predicting treatment-naive or αCD40-treated tissues, respectively. Red or blue dots indicate presence or absence, respectively, of the corresponding feature in the tissue. B-E. Box plots showing feature values for each of the top 15 features for models derived from T, IA, TAS, or NAP sites, respectively, split by treatment cohort. Each dot represents the log10+1 normalized feature value for one tissue region, inputted into the classifier model. Boxes = quartile 1 (Q1) to quartile 3 (Q3); whiskers = smallest and largest datapoints within 1.5*interquartile range (IQR) +/- Q3/Q1; solid line = median. Mann–Whitney U-test used to determine statistical significance. P-values corrected using the Benjamini–Hochberg procedure. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. B. T site, n= 55 treatment-naive and n = 48 αCD40-treated regions per feature. C. IA site, n= 89 treatment-naive and n = 43 αCD40-treated regions per feature. D. TAS site, n = 25 treatment-naive and n = 27 αCD40-treated regions per feature. E. NAP site, n = 6 treatment-naive and n = 13 αCD40-treated regions per feature.</p

Supplementary Table S2 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S2. Tissue area sampled per patient out of total tissue area resected per patient (in mm2).</p

Supplementary Table S6 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S6. Quantification of Cell-Cell Spatial Interactions.</p

Supplementary Figure S1 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S1. A. Stacked bar chart showing percent tissue area (out of 100) sampled per resection from each patient. Bars are colored by histopathologic site of the regions sampled. B. Representative IHC staining of each antibody used in sequence in the panel. Scale bar = 50 μm. C. Two representative regions stained with CD3, CD8, and CD4 antibodies. For each region, top images show gates for CD8 on CD3+ population (left) and CD4 on CD3+ CD8- population (right), and bottom row shows pseudo-colored mIHC images. D. Hierarchical gating template used to phenotype cells using image gating cytometry in FCS Image Cytometry RUO.</p