<p>XLS file, 48K, Supplemental Figure 1: (A) Laser microdissection in an IPMN (hematoxylin and eosin; original magnification: 10x); (B) After precise marking and microdissection the neoplastic epithelium is catapulted into a sterile tube for subsequent RNA extraction. (C) The section after dissection. Supplemental Figure 2: Detailed diagram of the study design, describing experimental setup of FFPE (FTS) and cyst fluid (CFS) studies, including number of specimens used and number of miRNA candidates identified in the course of each study. Supplemental Figure 3: Mean Cts for all FTS and CFS specimens. (A) Megaplex RT-qPCR data for FTS. (B) and (C) Singleplex RT-qPCR data for FTS. (D) Megaplex RT-qPCR data for CFS. (E) and (F) Singleplex RT-qPCR data for CFS. Supplemental Figure 4: Boxplots showing raw Ct values for Megaplex (A) and singleplex (B) RT-qPCR expression analyses of FTS1 and FTS1 plus FTS2, respectively. The diagnosis associated with each sample is indicated by color, while the FTS1 and FTS2 specimens are depicted as separate panels in the singleplex boxplot (B). Supplemental Figure 5: Boxplots showing raw Ct values for Megaplex (A) and singleplex (B) RT-qPCR expression analyses of cyst fluid specimens from the CFS1 and CFS1 plus CFS2, respectively. Diagnosis is indicated by color, the CFS1 and CFS2 specimens by panel. Supplemental Figure 6: Boxplots of raw Ct values for singleplex RT-qPCR expression analysis of the CFS1 and CFS2 specimens with reassignment to test or training set indicated by separation of panels. Supplemental Figure 7: Raw Ct values of miRNAs involved in DiffPairs comprising the logistic regression model in the CFS1 and CFS2 specimens. Raw Ct for miR-21, which is not a part of the logistic regression model, are also shown. Supplemental Figure 8: PCA applied to raw Cts (A) and restricted mean-center normalized Cts. (B) CFS1 and CFS2 singleplex RT-qPCR data. Note that CFS1 and CFS2 specimens do not separate in either plot.</p>
<p>XLS file, 48K, Supplemental Figure 1: (A) Laser microdissection in an IPMN (hematoxylin and eosin; original magnification: 10x); (B) After precise marking and microdissection the neoplastic epithelium is catapulted into a sterile tube for subsequent RNA extraction. (C) The section after dissection. Supplemental Figure 2: Detailed diagram of the study design, describing experimental setup of FFPE (FTS) and cyst fluid (CFS) studies, including number of specimens used and number of miRNA candidates identified in the course of each study. Supplemental Figure 3: Mean Cts for all FTS and CFS specimens. (A) Megaplex RT-qPCR data for FTS. (B) and (C) Singleplex RT-qPCR data for FTS. (D) Megaplex RT-qPCR data for CFS. (E) and (F) Singleplex RT-qPCR data for CFS. Supplemental Figure 4: Boxplots showing raw Ct values for Megaplex (A) and singleplex (B) RT-qPCR expression analyses of FTS1 and FTS1 plus FTS2, respectively. The diagnosis associated with each sample is indicated by color, while the FTS1 and FTS2 specimens are depicted as separate panels in the singleplex boxplot (B). Supplemental Figure 5: Boxplots showing raw Ct values for Megaplex (A) and singleplex (B) RT-qPCR expression analyses of cyst fluid specimens from the CFS1 and CFS1 plus CFS2, respectively. Diagnosis is indicated by color, the CFS1 and CFS2 specimens by panel. Supplemental Figure 6: Boxplots of raw Ct values for singleplex RT-qPCR expression analysis of the CFS1 and CFS2 specimens with reassignment to test or training set indicated by separation of panels. Supplemental Figure 7: Raw Ct values of miRNAs involved in DiffPairs comprising the logistic regression model in the CFS1 and CFS2 specimens. Raw Ct for miR-21, which is not a part of the logistic regression model, are also shown. Supplemental Figure 8: PCA applied to raw Cts (A) and restricted mean-center normalized Cts. (B) CFS1 and CFS2 singleplex RT-qPCR data. Note that CFS1 and CFS2 specimens do not separate in either plot.</p>
Supplementary Data from Covalent JNK Inhibitor, JNK-IN-8, Suppresses Tumor Growth in Triple-Negative Breast Cancer by Activating TFEB- and TFE3-Mediated Lysosome Biogenesis and Autophagy
Supplementary Data from Covalent JNK Inhibitor, JNK-IN-8, Suppresses Tumor Growth in Triple-Negative Breast Cancer by Activating TFEB- and TFE3-Mediated Lysosome Biogenesis and Autophagy
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
To determine whether DNA methylation patterns in genes coding for selected T-lymphocyte proteins are associated with perinatal psychiatric distress or with complications of pregnancy.T lymphocyte DNA was obtained from pregnant women across three time points in pregnancy and the postpartum period and epigenetic patterns were assessed using Illumina 450 K Methylation Beadchips. Seven selected genes critical for T cell function were analyzed for methylation changes during pregnancy and for associations of methylation patterns with psychiatric distress or with pregnancy complications, with particular attention paid to spatial aggregations of methyl groups, termed 'hotspots,' within the selected genes.In the candidate gene approach, DNA methylation density within a single cluster of 9 contiguous CpG loci within the CD3 gene was found to be strongly associated with anxiety and depression in mid- and late pregnancy, and weakly associated with the presence of complications of pregnancy. Average DNA methylation density across each of the seven genes examined, and assay-wide, was found to be relatively stable across pregnancy and postpartum, but methylation within the CD3 hotspot was more malleable and changes over time were coordinated across the nine cytosines in the hotspot. CD3 CpGs did not pass array-wide tests for significance, but CpG clusters in two other genes, DTNBP1 and OXSR1, showed array-wide significant associations with anxiety.Despite the need for tolerating the fetal hemi-allograft, overall DNA methylation patterns in T lymphocytes are generally stable over the mid to late course of human pregnancies and postpartum. However, site-specific changes in DNA methylation density in CD3 appear linked to both symptoms of depression and anxiety in pregnancy and, less strongly, to adverse pregnancy outcomes.
e18669 Background: A review of the North American Cancer Registries (NAACCR) found that only 66.4% of Stage I, II NSCLC patients received curative surgery. Mapping treatment would reveal the neighborhoods with undertreated patients. Central Texas is ideal for mapping with no competing causes for clustering of cases, no EPA superfund sites and radon is distributed evenly. Aim: Define the location of NSCLC cases by staging, treatment, and survival in the Austin Metro area to guide targeted interventions. Methods: Cancer Registries at Seton and St David’s Hospitals were queried for NSCLC Diagnosed 2004-2014, Age < 80, Address in 5 county metro, excluding resident of chronic care facility, homeless. A total of 2822 patients were identified (Stage I,II = 759, Stage III = 465, Stage IV = 941, and 99 = 657) . Using the registry records each case was coded for initial treatment. The hierarchy for assigning treatment was Chest Surgery, Radiation, Chemotherapy, Metastasectomy. Combined treatment cases were assigned to the higher modality. Results: There was a minor difference from reported curative surgery rate 67% (n = 511) for Stage I, II patients with 11% (n = 82) radiation. Only 16% (n = 119) Stage I, II subjects had no treatment. Forty six percent of Stage III (n = 465) were treated for cure with combined modality therapy, 25% (105) had no treatment. Registrars code 99-unstageable when there is TX, NX, or MX. This group had 46% (n = 304) no treatment patients compared to 35% (n = 329) in stage IV. Overall 30% (n = 857) patients had no treatment, cancer registry entries indicated 48 refused treatment and 70 were considered too fragile to treat. In the entire population, 2% (n = 63) were referred out for initial treatment. Maps comparing treated and untreated patients by stage and follow up days were produced. Conclusions: It is possible to map treatment, stage, and survival. This information should be useful in implementing targeted reduction of the burden of care, screening, and health literacy interventions.
