Project description:In this study, the efficiency of four RNA extraction methods was compared on 23 FFPE cardiac tissue specimens. The Qiagen AllPrep DNA/RNA FFPE kit (Method QP), Qiagen AllPrep DNA/RNA FFPE kit, with protocol modification on the ethanol wash step after deparaffinization (Method QE), CELLDATA RNAstorm 2.0 FFPE RNA Extraction kit (Method BP) and CELLDATA RNAstorm 2.0 FFPE RNA Extraction Kit with protocol modifications on the lysis step (Method BL). In comparing RNA quality metrics across FFPE RNA extract, nucleic acids extracted with Method QE and QP had the highest RNA yield. However, Method QE outperformed Method QP as more extract from Method QE had DV 200 values above 30%. Both method BL and BP produced a similar range of RNA purity and yield but more extract from Method BL had DV 200 values above 30% compared to Method BP. When accessing distribution value, Method BL outperformed Methods BP, QE, and QP as more extracts from Method BL had DV 200 values above 30% (16/23 samples) compared to other methods (PDV200<0.001; Kruskal-Wallis). However, method QE outperformed other methods in terms of RNA yield. The sequencing performance of RNA extracts from Method QE and Method BL was further tested on 8 matching samples with high RNA yield and high DV200 value respectively. RNA extracts from Method QE which yielded the highest RNA quantity among all methods exhibited comparable sequencing performance to extract obtained through Method BL, which yielded extract with high DV200 value. This study suggests that the DV200 and RNA yield are both reliable pre-analytic metrics in determining a suitable method for successful transcriptome sequencing of FFPE samples and have important implications for future studies exploring transcriptome sequencing of FFPE cardiac specimens.
Project description:Hela test samples prepared by FASP digestion were run on QE and QE-HF mass spectrometry as quality controls. We tried to compare the protein and peptide identification results between the two machines.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:Low coverage whole genome sequencing (lc-WGS) from inducible Tet TKO (Tet iTKO) and control (Ctrl) mouse ESCs (mESC), as well as for germline Dnmt TKO mESCs. mESCs were sorted to isolate the Live/Dead dye and Thy1.2 negative CD326+GFP+ population representing the mESCs populations responsive to the tamoxifen treatment. The cells were resuspended in FACS buffer and filtered with a 70 µM filter before sorting. These bulk-population samples were analyzed by using low coverage Whole Genome Sequencing (lc-WGS).
Project description:To investigate changes in noradrenergic neurons of the LC following infusion of human Tyrosinase (hTyr) We performed gene expression profiling analysis using data obtained from RNA-seq of Pre-IP and IP samples following hTyr or EYFP infusion into the LC.
Project description:To find out the binding partners of EhRacM, crosslinking and coimmunoprecipitation were performed. Then, eluted samples were submitted to Mass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine and analyzed by mass spctrometry (shotgun proteomics). The coimmunoprecipitation and following analysis were conducted three times.You can find the data of HA-EhRacM (1st) from "2022_7_32_54_PM_FileSize_500998639_Byte_F010303.mzid.gz", "2022_7_32_54_PM_FileSize_500998639_Byte_F010303.mzid_2022_7_32_54_PM_File_Size__500998639__Byte__F010303.MGF", "RO-CS-LE_220912_NozakiT_MS_Rac16_WT.mzML", and "RO-CS-LE_220912_NozakiT_MS_Rac16_WT.RAW". You can find the data of pEhExHA (mock) (1st) from "2022_5_02_01_AM_FileSize_662636563_Byte_F010297.mzid.gz", "2022_5_02_01_AM_FileSize_662636563_Byte_F010297.mzid_2022_5_02_01_AM_File_Size__662636563__Byte__F010297.MGF", "RO-CS-LE_220912_NozakiT_MS_pEhExHA.mzML", and "RO-CS-LE_220912_NozakiT_MS_pEhExHA.RAW".You can find the data of HA-EhRacM (2nd) from "2022_7_35_46_AM_FileSize_794949236_Byte_F009769.mzid.gz", "2022_7_35_46_AM_FileSize_794949236_Byte_F009769.mzid_2022_7_35_46_AM_File_Size__794949236__Byte__F009769.MGF", "JS-QE-CS_221108_NozakiT_MS_S1_RA.msf", "JS-QE-CS_221108_NozakiT_MS_S1_RA.RAW", "JS-QE-CS_221108_NozakiT_MS_S1_RA.mzML". You can find the data of pEhExHA (mock) (2nd) from "2022_2_58_28_AM_FileSize_846222756_Byte_F009771.mzid.gz", "2022_2_58_28_AM_FileSize_846222756_Byte_F009771.mzid_2022_2_58_28_AM_File_Size__846222756__Byte__F009771.MGF", "JS-QE-CS_221108_NozakiT_MS_S4_RA.msf", "JS-QE-CS_221108_NozakiT_MS_S4_RA.RAW", and "JS-QE-CS_221108_NozakiT_MS_S4_RA.mzML". You can find the data of HA-EhRacM (3rd) from "2023_12_15_11_AM_FileSize_1380630557_Byte_F012916.mzid.gz", "2023_12_15_11_AM_FileSize_1380630557_Byte_F012916.mzid_2023_12_15_11_AM_File_Size__1380630557__Byte__F012916.MGF", "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.msf", "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.mzML", and "JS-E480-CS_230518_NozakiT_MS_S1_DDA_10pct.RAW". You can find the data of pEhExHA (mock) (3rd) from "2023_6_35_51_PM_FileSize_1732075728_Byte_F012915.mzid.gz", "2023_6_35_51_PM_FileSize_1732075728_Byte_F012915.mzid_2023_6_35_51_PM_File_Size__1732075728__Byte__F012915.MGF", "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.msf", "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.mzML", and "JS-E480-CS_230518_NozakiT_MS_S4_DDA_10pct.RAW".
Project description:Background and Aims: The pathobiology of the non-destructive inflammatory bowel disease (IBD) lymphocytic colitis (LC) is poorly understood. Our aim was to define a LC-specific transcriptome to gain insight into LC pathology, identify genetic signatures uniquely linked to LC, and uncover potentially druggable disease pathways. Methods: We performed whole mucosa bulk RNA-sequencing of LC and CC samples from patients with active disease, and healthy controls (n=4-10 per cohort). Differential gene expression was analyzed by gene-set enrichment and deconvolution analyses to identify pathologically relevant pathways and cells, respectively, altered in LC. Key findings were validated using reverse transcription quantitative PCR and/or immunohistochemistry. Finally, we compared our sequencing data to a previous cohort of ulcerative colitis and Crohn’s disease patients (n=4 per group) to distinguish non-destructive from classic IBD. Results: The LC-specific transcriptome was defined by a limited mucosal immune response against microbiota compared to CC and classic IBD samples. In contrast, we noted a distinct induction of regulatory non-coding RNA species in LC samples. Moreover, compared to CC, we observed decreased water channel and cell adhesion molecule gene expression, which was associated with reduced intestinal epithelial cell proliferation. Conclusions: We conclude that LC is a pathomechanistically distinct disease that is characterized by a dampened immune response despite massive mucosal immune cell infiltration. Our results point to regulatory micro-RNAs as a potential disease-specific feature that may be amenable to therapeutic intervention.