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"Serum-Proteomics-Pre-diabetic/index.qmd" + - "Serum-Proteomics-Atherosclerosis/index.qmd" + - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd" + - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" + - "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd" + sort: "date desc" + categories: true +toc-title: Year +toc-location: right +date-format: "MMMM D, YYYY" +image: "" +code-tools: false +comments: false +--- + +Welcome to my blog, here, you will find a collection of posts for some of the previously published articles! + +The snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar ID. + +![](/img/wordcloud.png){width="500"} diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6 b/.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6 similarity index 82% rename from .Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6 rename to .Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6 index ed34d1b..5a81066 100644 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6 +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6 @@ -3,7 +3,7 @@ "path": "~/GitHub/santoshdbhosale.github.io/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd", "project_path": "blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd", "type": "quarto_markdown", - "hash": "0", + "hash": "3587759168", "contents": "", "dirty": false, "created": 1714345743323.0, @@ -12,8 +12,8 @@ "properties": { "source_window_id": "", "Source": "Source", - "cursorPosition": "6,22", - "scrollLine": "0", + "cursorPosition": "13,529", + "scrollLine": "11", "docOutlineVisible": "1", "rmdVisualMode": "false", "rmdVisualWrapConfigured": "true", @@ -21,11 +21,11 @@ "rmdVisualModeLocation": "146:323.3333435058594" }, "folds": "", - "lastKnownWriteTime": 1714353834, + "lastKnownWriteTime": 1716418074, "encoding": "UTF-8", "collab_server": "", "source_window": "", - "last_content_update": 1714353834257, + "last_content_update": 1716418074841, "read_only": false, "read_only_alternatives": [] } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6-contents new file mode 100644 index 0000000..b3f5d46 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6-contents @@ -0,0 +1,15 @@ +--- +title: "Serum Proteomics of mother infant dyads: T1D risk" +date: 2024-04-28 +categories: + - publications + - serum +image: fig/Study_design.png +--- + +**Background:** +The physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata. + +**Methods:** +Undepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R. + diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118 b/.Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB similarity index 76% rename from .Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118 rename to .Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB index 6e93ee8..fe8ef5b 100644 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118 +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB @@ -1,18 +1,18 @@ { - "id": "8B9F7118", + "id": "583AEBDB", "path": "~/GitHub/santoshdbhosale.github.io/blog/index.qmd", "project_path": "blog/index.qmd", "type": "quarto_markdown", - "hash": "107782491", + "hash": "2153559105", "contents": "", "dirty": false, - "created": 1714540303705.0, + "created": 1716425089478.0, "source_on_save": false, - "relative_order": 4, + "relative_order": 2, "properties": { "source_window_id": "", "Source": "Source", - "cursorPosition": "10,68", + "cursorPosition": "11,63", "scrollLine": "6", "rmdVisualMode": "false", "rmdVisualWrapConfigured": "true", @@ -22,11 +22,11 @@ "docOutlineSize": "31" }, "folds": "", - "lastKnownWriteTime": 1714540339, + "lastKnownWriteTime": 1716425097, "encoding": "UTF-8", "collab_server": "", "source_window": "", - "last_content_update": 1714540339126, + "last_content_update": 1716425097574, "read_only": false, "read_only_alternatives": [] } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB-contents similarity index 93% rename from .Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118-contents rename to .Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB-contents index bda5818..537b61b 100644 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118-contents +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB-contents @@ -9,6 +9,7 @@ listing: - "Serum-Proteomics-Atherosclerosis/index.qmd" - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd" - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" + - "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index" sort: "date desc" categories: true toc-title: Year diff --git a/.Rproj.user/224B9E6C/sources/session-b6384cda/B103A6A6-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/B103A6A6-contents new file mode 100644 index 0000000..ef28a46 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/B103A6A6-contents @@ -0,0 +1,21 @@ +--- +title: "Serum Proteomics Atherosclerosis" +date: 2020-11-11 +categories: + - publications + - serum +image: fig/roc-plot.png +--- +Atherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. +Myocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. +Nevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. + +To gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** +We performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis. + +The profiling results indicated differential abundances in a set of proteins. +Furthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. +Finally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). + +![](fig/roc-plot.png){} +More details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).** diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174 b/.Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753 similarity index 52% rename from .Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174 rename to .Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753 index 797d1dc..becd072 100644 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174 +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753 @@ -1,31 +1,32 @@ { - "id": "A21CD174", - "path": "~/GitHub/santoshdbhosale.github.io/blog/Serum-Proteomics-Pre-diabetic/index.qmd", - "project_path": "blog/Serum-Proteomics-Pre-diabetic/index.qmd", + "id": "C0CC5753", + "path": "~/GitHub/santoshdbhosale.github.io/about/index.qmd", + "project_path": "about/index.qmd", "type": "quarto_markdown", "hash": "0", "contents": "", "dirty": false, - "created": 1714540255280.0, + "created": 1716425158202.0, "source_on_save": false, "relative_order": 3, "properties": { "source_window_id": "", "Source": "Source", - "cursorPosition": "0,0", - "scrollLine": "12", - "rmdVisualMode": "true", + "cursorPosition": "9,189", + "scrollLine": "10", + "rmdVisualMode": "false", "rmdVisualWrapConfigured": "true", "docOutlineVisible": "1", "rmdVisualCollapsedChunks": "", - "rmdVisualModeLocation": "2499:1039.3333740234375" + "rmdVisualModeLocation": "1070:500", + "docOutlineSize": "72" }, "folds": "", - "lastKnownWriteTime": 1714588690, + "lastKnownWriteTime": 1716425249, "encoding": "UTF-8", "collab_server": "", "source_window": "", - "last_content_update": 1714588690856, + "last_content_update": 1716425249207, "read_only": false, "read_only_alternatives": [] } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753-contents new file mode 100644 index 0000000..9bf1fca --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753-contents @@ -0,0 +1,17 @@ +--- +about: + template: jolla + id: about-block +--- + +::: {#about-block} +::: + +I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, a commercial proteomics contract research organization owned by Cedars-Sinai Medical Center, Los Angeles, CA, USA. + +During my postdoctoral fellowship, I worked in Prof. Martin R. Larsen's lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples. + +I pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis. + +Before enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL. + diff --git a/.Rproj.user/224B9E6C/sources/per/u/CE2DE855-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/CE2DE855-contents similarity index 100% rename from .Rproj.user/224B9E6C/sources/per/u/CE2DE855-contents rename to .Rproj.user/224B9E6C/sources/session-b6384cda/CE2DE855-contents diff --git a/.Rproj.user/224B9E6C/sources/session-b6384cda/EC8B4BB9-contents b/.Rproj.user/224B9E6C/sources/session-b6384cda/EC8B4BB9-contents new file mode 100644 index 0000000..469aabc --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-b6384cda/EC8B4BB9-contents @@ -0,0 +1,6 @@ +data = read.delim("mg_kegg.tsv") +S1<- ggplot(data, aes(x= reorder (Pathways, +FDR), y= FDR, size=DEGs, color=FDR, group=Group)) + geom_point(alpha = 0.8) + + theme_classic() + coord_flip() +S1 = S1+scale_color_gradient(low = "red2", high = "mediumblue", space = "Lab") +S1 = S1 + labs(x = "Group", y = "KEGG pathways", title = "KEGG Pathways Enriched for Protein DE in ENRC and ENRI Cells") +S1 + facet_wrap(~Group, scales = "free") diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/lock_file b/.Rproj.user/224B9E6C/sources/session-b6384cda/lock_file similarity index 100% rename from .Rproj.user/224B9E6C/sources/session-b8d86596/lock_file rename to .Rproj.user/224B9E6C/sources/session-b6384cda/lock_file diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6-contents b/.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6-contents deleted file mode 100644 index 94ac764..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6-contents +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: "Serum Proteomics of mother infant dyads: T1D risk" -date: 2024-04-28 -categories: - - publications - - serum -image: fig/PCA_All.png ---- - - - - diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/38F1B9DD-contents b/.Rproj.user/224B9E6C/sources/session-b8d86596/38F1B9DD-contents deleted file mode 100644 index e69de29..0000000 diff --git a/.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174-contents b/.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174-contents deleted file mode 100644 index 228562f..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174-contents +++ /dev/null @@ -1,16 +0,0 @@ ---- -title: "Serum Proteomics Pre diabetic" -date: 2020-10-27 -categories: - - publications - - serum -image: fig/tsp.png ---- - -Type-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as **The Finnish Type-1 Diabetes Prediction and Prevention project [DIPP](https://dipp.fi/?page_id=5239&lang=en)** has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. **These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.** - -We utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum. - -Previous serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the [**first time serum proteomics profile of pre-diabetic children**](https://diabetes.diabetesjournals.org/content/64/6/2265), mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion. - -![](fig/tsp.png) diff --git a/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb6384cda/chunks.json b/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb6384cda/chunks.json new file mode 100644 index 0000000..d86241e --- /dev/null +++ b/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb6384cda/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1716425095} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb8d86596/chunks.json b/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb8d86596/chunks.json deleted file mode 100644 index 0ab5cbf..0000000 --- a/.Rproj.user/shared/notebooks/538639EF-index/1/224B9E6Cb8d86596/chunks.json +++ /dev/null @@ -1 +0,0 @@ -{"chunk_definitions":[],"doc_write_time":1714540318} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/538639EF-index/1/s/chunks.json b/.Rproj.user/shared/notebooks/538639EF-index/1/s/chunks.json index 0ab5cbf..d86241e 100644 --- a/.Rproj.user/shared/notebooks/538639EF-index/1/s/chunks.json +++ b/.Rproj.user/shared/notebooks/538639EF-index/1/s/chunks.json @@ -1 +1 @@ -{"chunk_definitions":[],"doc_write_time":1714540318} \ No newline at end of file +{"chunk_definitions":[],"doc_write_time":1716425095} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/A8C59ABA-index/1/224B9E6Cb6384cda/chunks.json b/.Rproj.user/shared/notebooks/A8C59ABA-index/1/224B9E6Cb6384cda/chunks.json new file mode 100644 index 0000000..64f3157 --- /dev/null +++ b/.Rproj.user/shared/notebooks/A8C59ABA-index/1/224B9E6Cb6384cda/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1716425223} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/A8C59ABA-index/1/s/chunks.json b/.Rproj.user/shared/notebooks/A8C59ABA-index/1/s/chunks.json index 745cd85..64f3157 100644 --- a/.Rproj.user/shared/notebooks/A8C59ABA-index/1/s/chunks.json +++ b/.Rproj.user/shared/notebooks/A8C59ABA-index/1/s/chunks.json @@ -1 +1 @@ -{"chunk_definitions":[],"doc_write_time":1707418385} \ No newline at end of file +{"chunk_definitions":[],"doc_write_time":1716425223} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb6384cda/chunks.json b/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb6384cda/chunks.json new file mode 100644 index 0000000..bb2b8ca --- /dev/null +++ b/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb6384cda/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1716417307} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb8d86596/chunks.json b/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb8d86596/chunks.json new file mode 100644 index 0000000..8fe1bea --- /dev/null +++ b/.Rproj.user/shared/notebooks/E3E4C871-index/1/224B9E6Cb8d86596/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1714596536} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/E3E4C871-index/1/s/chunks.json b/.Rproj.user/shared/notebooks/E3E4C871-index/1/s/chunks.json index 0dffcb9..bb2b8ca 100644 --- a/.Rproj.user/shared/notebooks/E3E4C871-index/1/s/chunks.json +++ b/.Rproj.user/shared/notebooks/E3E4C871-index/1/s/chunks.json @@ -1 +1 @@ -{"chunk_definitions":[],"doc_write_time":1714345755} \ No newline at end of file +{"chunk_definitions":[],"doc_write_time":1716417307} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/EF7F065A-index/1/224B9E6Cb8d86596/chunks.json b/.Rproj.user/shared/notebooks/EF7F065A-index/1/224B9E6Cb8d86596/chunks.json deleted file mode 100644 index cf1650f..0000000 --- a/.Rproj.user/shared/notebooks/EF7F065A-index/1/224B9E6Cb8d86596/chunks.json +++ /dev/null @@ -1 +0,0 @@ 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a/.quarto/idx/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd.json b/.quarto/idx/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd.json index 40a2b9a..bd2dda0 100644 --- a/.quarto/idx/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd.json +++ b/.quarto/idx/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd.json @@ -1 +1 @@ -{"title":"Serum Proteomics of mother infant dyads: T1D risk","markdown":{"yaml":{"title":"Serum Proteomics of mother infant dyads: T1D risk","date":"2024-04-28","categories":["publications","serum"],"image":"fig/PCA_All.png"},"containsRefs":false,"markdown":"\n\n\n\n\n","srcMarkdownNoYaml":"\n\n\n\n\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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citation:","title-block-author-single":"Author","title-block-author-plural":"Authors","title-block-affiliation-single":"Affiliation","title-block-affiliation-plural":"Affiliations","title-block-published":"Published","title-block-modified":"Modified","title-block-keywords":"Keywords","callout-tip-title":"Tip","callout-note-title":"Note","callout-warning-title":"Warning","callout-important-title":"Important","callout-caution-title":"Caution","code-summary":"Code","code-tools-menu-caption":"Code","code-tools-show-all-code":"Show All Code","code-tools-hide-all-code":"Hide All Code","code-tools-view-source":"View Source","code-tools-source-code":"Source Code","tools-share":"Share","tools-download":"Download","code-line":"Line","code-lines":"Lines","copy-button-tooltip":"Copy to Clipboard","copy-button-tooltip-success":"Copied!","repo-action-links-edit":"Edit this page","repo-action-links-source":"View source","repo-action-links-issue":"Report an issue","back-to-top":"Back to top","search-no-results-text":"No results","search-matching-documents-text":"matching documents","search-copy-link-title":"Copy link to search","search-hide-matches-text":"Hide additional matches","search-more-match-text":"more match in this document","search-more-matches-text":"more matches in this document","search-clear-button-title":"Clear","search-text-placeholder":"","search-detached-cancel-button-title":"Cancel","search-submit-button-title":"Submit","search-label":"Search","toggle-section":"Toggle section","toggle-sidebar":"Toggle sidebar navigation","toggle-dark-mode":"Toggle dark mode","toggle-reader-mode":"Toggle reader mode","toggle-navigation":"Toggle navigation","crossref-fig-title":"Figure","crossref-tbl-title":"Table","crossref-lst-title":"Listing","crossref-thm-title":"Theorem","crossref-lem-title":"Lemma","crossref-cor-title":"Corollary","crossref-prp-title":"Proposition","crossref-cnj-title":"Conjecture","crossref-def-title":"Definition","crossref-exm-title":"Example","crossref-exr-title":"Exercise","crossref-ch-prefix":"Chapter","crossref-apx-prefix":"Appendix","crossref-sec-prefix":"Section","crossref-eq-prefix":"Equation","crossref-lof-title":"List of Figures","crossref-lot-title":"List of Tables","crossref-lol-title":"List of Listings","environment-proof-title":"Proof","environment-remark-title":"Remark","environment-solution-title":"Solution","listing-page-order-by":"Order By","listing-page-order-by-default":"Default","listing-page-order-by-date-asc":"Oldest","listing-page-order-by-date-desc":"Newest","listing-page-order-by-number-desc":"High to Low","listing-page-order-by-number-asc":"Low to 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risk","date":"2024-04-28","categories":["publications","serum"],"image":"fig/PCA_All.png"},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file +{"title":"Serum Proteomics of mother infant dyads: T1D risk","markdown":{"yaml":{"title":"Serum Proteomics of mother infant dyads: T1D risk","date":"2024-04-28","categories":["publications","serum"],"image":"fig/Study_design.png"},"containsRefs":false,"markdown":"\n\n**Background:**\nThe physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata.\n\n**Methods:**\nUndepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R.\n\n","srcMarkdownNoYaml":"\n\n**Background:**\nThe physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata.\n\n**Methods:**\nUndepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R.\n\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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risk","date":"2024-04-28","categories":["publications","serum"],"image":"fig/Study_design.png"},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/idx/blog/index.qmd.json b/.quarto/idx/blog/index.qmd.json index dcafea1..61b7d03 100644 --- a/.quarto/idx/blog/index.qmd.json +++ b/.quarto/idx/blog/index.qmd.json @@ -1 +1 @@ -{"markdown":{"yaml":{"author":"","title-block-banner":false,"page-layout":"full","description-meta":"Welcome to my blog, Here, you will find a collection of posts for some of the previously published articles","listing":{"contents":["Serum-Proteomics-Pre-diabetic/index.qmd","Serum-Proteomics-Atherosclerosis/index.qmd","Mass-Spectrometry-Based-Serum-Proteomics/index.qmd","AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd"],"sort":"date desc","categories":true},"toc-title":"Year","toc-location":"right","date-format":"MMMM D, YYYY","image":"","code-tools":false,"comments":false},"containsRefs":false,"markdown":"\n\nWelcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\nThe snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar ID.\n\n![](/img/wordcloud.png){width=\"500\"}\n","srcMarkdownNoYaml":"\n\nWelcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\nThe snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar ID.\n\n![](/img/wordcloud.png){width=\"500\"}\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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desc","categories":true},"toc-title":"Year","toc-location":"right","date-format":"MMMM D, YYYY","image":"","code-tools":false,"comments":false},"containsRefs":false,"markdown":"\n\nWelcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\nThe snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar ID.\n\n![](/img/wordcloud.png){width=\"500\"}\n","srcMarkdownNoYaml":"\n\nWelcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\nThe snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar 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articles","listing":{"contents":["Serum-Proteomics-Pre-diabetic/index.qmd","Serum-Proteomics-Atherosclerosis/index.qmd","Mass-Spectrometry-Based-Serum-Proteomics/index.qmd","AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd","Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index"],"sort":"date desc","categories":true},"toc-title":"Year","toc-location":"right","date-format":"MMMM D, YYYY","image":"","comments":false},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/listing/listing-cache.json b/.quarto/listing/listing-cache.json index f095ec8..bf93742 100644 --- a/.quarto/listing/listing-cache.json +++ b/.quarto/listing/listing-cache.json @@ -4,7 +4,8 @@ "Serum-Proteomics-Pre-diabetic/index.qmd", "Serum-Proteomics-Atherosclerosis/index.qmd", "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd", - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" + "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd", + "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index" ] } } \ No newline at end of file diff --git a/.quarto/preview/lock b/.quarto/preview/lock index 85b5be1..e1424e2 100644 --- a/.quarto/preview/lock +++ b/.quarto/preview/lock @@ -1 +1 @@ -13576 \ No newline at end of file +10840 \ No newline at end of file diff --git a/.quarto/xref/0ecb96e6 b/.quarto/xref/0ecb96e6 index 7df77d5..208374b 100644 --- a/.quarto/xref/0ecb96e6 +++ b/.quarto/xref/0ecb96e6 @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/28836b25 b/.quarto/xref/28836b25 index 208374b..7df77d5 100644 --- a/.quarto/xref/28836b25 +++ b/.quarto/xref/28836b25 @@ -1 +1 @@ -{"entries":[],"headings":[]} \ No newline at end of file +{"headings":[],"entries":[]} \ No newline at end of file diff --git a/.quarto/xref/375be950 b/.quarto/xref/375be950 index 208374b..7df77d5 100644 --- a/.quarto/xref/375be950 +++ b/.quarto/xref/375be950 @@ -1 +1 @@ -{"entries":[],"headings":[]} \ No newline at end of file +{"headings":[],"entries":[]} \ No newline at end of file diff --git a/.quarto/xref/52dc296d b/.quarto/xref/52dc296d index 7df77d5..208374b 100644 --- a/.quarto/xref/52dc296d +++ b/.quarto/xref/52dc296d @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/about/index.qmd b/about/index.qmd index 694e2eb..9ffd687 100644 --- a/about/index.qmd +++ b/about/index.qmd @@ -7,7 +7,7 @@ about: ::: {#about-block} ::: -I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA. +I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, a commercial proteomics contract research organization owned by Cedars-Sinai Medical Center, Los Angeles, CA, USA. During my postdoctoral fellowship, I worked in Prof. Martin R. Larsen's lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples. diff --git a/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/PCA_All.png b/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/PCA_All.png deleted file mode 100644 index aee5684..0000000 Binary files a/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/PCA_All.png and /dev/null differ diff --git a/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/Study_design.png b/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/Study_design.png new file mode 100644 index 0000000..bb36640 Binary files /dev/null and b/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/fig/Study_design.png differ diff --git a/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd b/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd index 94ac764..b3f5d46 100644 --- a/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd +++ b/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd @@ -4,9 +4,12 @@ date: 2024-04-28 categories: - publications - serum -image: fig/PCA_All.png +image: fig/Study_design.png --- +**Background:** +The physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata. - +**Methods:** +Undepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R. diff --git a/blog/index.qmd b/blog/index.qmd index 040e23a..4b653e0 100644 --- a/blog/index.qmd +++ b/blog/index.qmd @@ -9,6 +9,7 @@ listing: - "Serum-Proteomics-Atherosclerosis/index.qmd" - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd" - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" + - "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index" sort: "date desc" categories: true toc-title: Year diff --git a/docs/about/index.html b/docs/about/index.html index b938407..d11cfd4 100644 --- a/docs/about/index.html +++ b/docs/about/index.html @@ -144,7 +144,7 @@ -

I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

+

I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, a commercial proteomics contract research organization owned by Cedars-Sinai Medical Center, Los Angeles, CA, USA.

During my postdoctoral fellowship, I worked in Prof. Martin R. Larsen’s lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.

I pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.

Before enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL.

diff --git a/docs/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html b/docs/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html index 72a6bb5..c333660 100644 --- a/docs/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html +++ b/docs/blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html @@ -167,6 +167,8 @@

Serum Proteomics of mother infant dyads: T1D risk

+

Background: The physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata.

+

Methods: Undepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R.

diff --git a/docs/search.json b/docs/search.json index d15788c..e97e713 100644 --- a/docs/search.json +++ b/docs/search.json @@ -39,7 +39,7 @@ "href": "about/index.html", "title": "Santosh D. Bhosale", "section": "", - "text": "I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.\nDuring my postdoctoral fellowship, I worked in Prof. Martin R. Larsen’s lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.\nI pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.\nBefore enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL." + "text": "I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, a commercial proteomics contract research organization owned by Cedars-Sinai Medical Center, Los Angeles, CA, USA.\nDuring my postdoctoral fellowship, I worked in Prof. Martin R. Larsen’s lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.\nI pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.\nBefore enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL." }, { "objectID": "blog/index.html", @@ -55,6 +55,13 @@ "section": "", "text": "Mass spectrometry based proteomics is a versatile technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the comprehensive overview of modern proteomics.\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\nQuality control checks.\nDatabase search and quantitative analysis.\nStatistical analysis\nFunctional annotation analysis\n\nIn this blog post, I will highlight the exploratory analysis tools used to process the mass spectrometry data. Such analysis gives an overview of high-dimensional biological data, guidance for further analysis and hypothesis testing. The tools include free and proprietary softwares.\n\nQuality control checks: Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\nOften times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\nDDA analysis\n\nRawMeat: developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\nRawBeans: generates an interactive html report for\nQuiC ™: Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requires addition of iRT peptides to the samples." }, + { + "objectID": "blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html", + "href": "blog/Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.html", + "title": "Serum Proteomics of mother infant dyads: T1D risk", + "section": "", + "text": "Background: The physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata.\nMethods: Undepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R." + }, { "objectID": "cv/index.html", "href": "cv/index.html",