Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning

The cascade of immune responses involves activation of diverse immune cells and release of a large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present an approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after 1 day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers the direction of single-cell omics and could be widely applicable to human diseases

Supplementary Tables from FTO-Dependent <i>N</i>⁶-Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling

Supplemental tables include gene lists related to Figure 5, additional experimental data related to Figure S3I, and primers sequence. Table S1. Numbers of spheroids were counted for each cell dilution; Table S2. List of 20 genes that exhibit a significant change between control and FTO-overexpressing OVCAR5 cells in m6A peak levels, and abundance of the corresponding mRNA transcripta; Table S3. List of primers oligonucleotides; Table S4. Summary of the m6A-seq and RNA-seq; Table S5. List of top 50 hypo-methylation & down-regulated genes; Table S6: List of top 50 hypo-methylation &up-regulated genes.</p

Data_Sheet_1_Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients.docx

Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.</p

Supplementary Figures from FTO-Dependent <i>N</i>⁶-Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling

Additional experimental results supporting the main figures are included. Figure S1. Expression of key RNA methylation regulators in OC; Figure S2. Flow cytometry analysis for ALDH and CD133 in single cell suspensions derived from human ovarian tumors; Figure S3. FTO overexpression in OC cells; Figure S4. FTO overexpression decreases spheroid formation ability of OC cells dissociated from xenografts; Figure S5. m6A activity and tumor initiation capacity; Figure S6. Gene sets enriched in FTO vs. control OC cells; Figure S7. Expression of PDE4B and PDE1C in FTO overexpressed OC cells; Figure S8. The Genome Browser visualizes the predicted motif binding sites for proteins (IGF2BP2 and IGF2BP3) on the input sequence; Figure S9. Effects of phosphodiesterase inhibitors on stemness features are dependent on FTO; Figure S10. Quality control for RNA-seq; Figure S11. Quality control for MeRIP-seq</p

Supplementary Methods from FTO-Dependent <i>N</i>⁶-Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling

Detailed information regarding materials and methods are included in this file.</p

Supplementary Tables 1 and 2, Figures 1-9 from Sprouty2 Drives Drug Resistance and Proliferation in Glioblastoma

Supplementary Tables 1 and 2, Figures 1-9. Supplemental Table 1. H-score analysis for SPRY2 staining in 10 EGFRvIII-negative and 10 EGFRvIII-positive tumors. Supplemental Table 2. Upregulated genes shared by human GBMs expressing EGFRvIII and 9L.EGFRvIII rat tumors compared to wild-type EGFR human GBMs or 9L.EV rat tumors. Supplemental Figure 1. SPRY2 is efficiently knocked down by shRNA expression in GBM cell lines. Supplemental Figure 2. SPRY2 knockdown by a second non-overlapping shRNA reduces cellular proliferation, and SPRY2 knockdown by transient siRNA transfection reduces colony formation in soft agar in EGFRvIII-expressing cells. Supplemental Figure 3. SPRY2 knockdown increases cellular sensitivity to EGFR and c-MET co-inhibition. Supplemental Figure 4. SPRY2 knockdown promotes response to EGFR and c-MET coinhibition in GSC cells. Supplemental Figure 5. p38 and JNK control anchorage-independent growth and response to EGFR and c-MET co-inhibition. Supplemental Figure 6. shRNA-mediated knockdown of MKP-1 or MKP-5 reduces MKP-1 or MKP-5 mRNA level. Supplemental Figure 7. SPRY2 protein expression in kidney and cerebellum sections by immunohistochemical analysis. Supplemental Figure 8. SPRY2 correlates well with ERK phosphorylation in a panel of GBM cell lines. Supplemental Figure 9. TCGA GBM dataset analysis reveals that SPRY2 expression is associated with reduced patient survival.</p

Supplementary Material and Methods from Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis

Supplemental Methods and Material</p

Supplementary Figures from Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis

Supplementary Figures S1, S2, S3, S4, S5, S6, S7 and S8</p

Supplementary Table from Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis

Supplementary Table S1, S2, S3, and S4</p

Supplementary Table S1 from Prognostic DNA Methylation Biomarkers in Ovarian Cancer

Supplementary Table S1 from Prognostic DNA Methylation Biomarkers in Ovarian Cance