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| 2. |
- Bäck, Peter, et al.
(author)
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Automated PreScan function for scanning fluorescently stained 2D-PAGE gels
- 2005
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 4:5, s. 1511-1515
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Journal article (peer-reviewed)abstract
- To automate the acquisition of images from fluorescently stained gels, the power of the excitation laser(s) must be optimized for each sample to prevent spot saturation (or to allow unimportant spots to saturate) yet still retaining sensitivity. In this work, we describe the implementation and effectiveness of a pre-scan function in a robotic solution for the automation of 2D gel scanning.
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| 3. |
- Bäck, Peter, et al.
(author)
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Automating gel image acquisition
- 2003
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 2:6, s. 662-664
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Journal article (peer-reviewed)abstract
- We describe the design and implementation of a robotic solution to automate the acquisition of gel images. The soft- and hardware aspects are outlined together with the various safety aspects that need to be addressed.
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| 4. |
- Cifani, Paolo, et al.
(author)
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Molecular Portrait of Breast-Cancer-Derived Cell Lines Reveals Poor Similarity with Tumors.
- 2015
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 14:7, s. 2819-2827
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Journal article (peer-reviewed)abstract
- Breast-cancer-derived cell lines are an important sample source for cancer proteomics and can be classified on the basis of transcriptomic analysis into subgroups corresponding to the molecular subtypes observed in mammary tumors. This study describes a tridimensional fractionation method that allows high sequence coverage and proteome-wide estimation of protein expression levels. This workflow has been used to conduct an in-depth quantitative proteomic survey of five breast cancer cell lines matching all major cancer subgroups and shows that despite their different classification, these cell lines display a very high level of similarity. A proteome-wide comparison with the RNA levels observed in the same samples showed very little to no correlation. Finally, we demonstrate that the proteomes of in vitro models of breast cancer display surprisingly little overlap with those of clinical samples.
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| 5. |
- Furst, Camilla Melin, et al.
(author)
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Quantitative mass spectrometry to study inflammatory cartilage degradation and resulting interactions with the complement system
- 2016
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In: Journal of Immunology. - : The American Association of Immunologists. - 0022-1767 .- 1550-6606. ; 197:8, s. 3415-3424
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Journal article (peer-reviewed)abstract
- Joint diseases are often characterized by inflammatory processes that result in pathological changes in joint tissues, including cartilage degradation and release of components into the synovial fluid. The complement system plays a central role in promoting the inflammation. Because several cartilage proteins are known to interact with complement, causing either activation or inhibition of the system, we aimed to investigate these interactions comprehensively. Bovine cartilage explants were cultured with IL-1α to induce cartilage degradation, followed by incubation with human serum. Label-free selected reaction monitoring mass spectrometry was used to specifically quantify complement proteins interacting with the cartilage explant. In parallel, the time-dependent degradation of cartilage was detected using mass spectrometry analysis (liquid chromatography-tandem mass spectrometry). Complement proteins resulting from activation of the classical, alternative, and terminal pathways were detected on IL-1α-stimulated cartilage at time points when clear alterations in extracellular matrix composition had occurred. Increased levels of the complement activation product C4d, as detected by ELISA in serum after incubation with IL-1α-stimulated cartilage, confirmed the selected reaction monitoring results indicating complement activation. Further, typical activated (cleaved) C3 fragments were detected by Western blotting in extracts of IL-1α-stimulated cartilage. No complement activation was triggered by cartilage cultured in the absence of IL-1α. Components released from IL-1α-stimulated cartilage during culture had an inhibitory effect on complement activation. These were released after a longer incubation period with IL-1α and may represent a feedback reaction to cartilage-triggered complement activation observed after a shorter incubation period.
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| 6. |
- Hulteberg, Christian, et al.
(author)
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Frågeställningar i examensarbeten
- 2014
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In: [Host publication title missing].
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Conference paper (other academic/artistic)abstract
- För att klara examinationsmålen i civilingenjörsexamen (SFS 1993:100 bilaga 2) ska varje student visa förmåga att med helhetssyn kritiskt, självständigt och kreativt identifiera, formulera och hantera komplexa frågeställningar. För att öka förståelsen för hur forskningsfrågan identifieras, formuleras och hanteras vid LTH har ett tjugotal examensarbetsrapporter från olika institutioner granskats. Granskningen har utgått från rapporternas inledande och avslutande kapitel för att se om forskningsfrågan tydligt formuleras i inledningen och om den besvaras i slutsatserna. Utifrån det material vi har studerat och publicerad litteratur kan vi konstatera att det är ett allmänt problem att studenter på masters-nivå överlag har liten vana vid att hantera komplexa frågeställningar. Det tycks finnas bristande kunskaper om vad vetenskaplighet/vetenskaplig metodik/vetenskaplig tradition innebär för vad som ska presenteras och vi kan konstatera att det både på LTH och i stort finns ett behov för åtgärder för att på ett mer effektiv sätt träna studenter i detta. Att litteraturen inom detta område är begränsad visar på både behovet men kanske också svårigheter att identifiera precis hur detta kan göras. Vi identifierade detta som ett viktigt utvecklingsområde för handledare av examensarbeten inom civilingenjörsutbildningarna på LTH, som dock också kan komma att behöva stöd av nya strukturer och systematiska åtgärder för att nå gemensamma mål för hela fakulteten.
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| 7. |
- Krogh, Morten, et al.
(author)
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Analysis of DIGE data using a linear mixed model allowing for protein-specific dye effects
- 2007
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In: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 7:23, s. 4235-4244
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Journal article (peer-reviewed)abstract
- Abstract in UndeterminedDifferential in-gel electrophoresis (DIGE) experiments allow three protein samples to be run per gel. The three samples are labeled with the spectrally resolvable fluorescent dyes, Cy2, Cy3, and Cy5, respectively. Here, we show that protein-specific dye effects exist, and we present a linear mixed model for analysis of DIGE data which takes dye effects into account. A Java implementation of the model, called DIGEanalyzer, is freely available at http://bioinfo.thep.lu.se/digeanalyzer.html. Three DIGE experiments from our laboratory, with 173, 64, and 24 gels, respectively, were used to quantify and verify the dye effects. DeCyder 5.0 and 6.5 were used for spot detection and matching. The fractions of proteins with a statistically significant (0.001 level) dye effect were 19, 34, and 23%, respectively. The fractions of proteins with a dye effect above 1.4-fold change were 1, 4, and 6%, respectively. The median magnitude of the dye effect was 1.07-fold change for Cy5 versus Cy3 and 1.16-fold change for Cy3 versus Cy2. The maximal dye effect was a seven-fold change. The dye effects of spots corresponding to the same protein tend to be similar within each of the three experiments, and to a smaller degree across experiments.
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| 8. |
- Kurbasic, Emila, et al.
(author)
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Changes in glycoprotein expression between primary breast tumour and synchronous lymph node metastases or asynchronous distant metastases.
- 2015
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In: Clinical Proteomics. - : Springer Science and Business Media LLC. - 1559-0275 .- 1542-6416. ; 12:1
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Journal article (peer-reviewed)abstract
- Breast cancer is a very heterogeneous disease and some patients are cured by the surgical removal of the primary tumour whilst other patients suffer from metastasis and spreading of the disease, despite adjuvant therapy. A number of prognostic and treatment predictive factors have been identified such as tumour size, oestrogen (ER) and progesterone (PgR) receptor status, human epidermal growth factor receptor type 2 (HER2) status, histological grade, Ki67 and age. Lymph node involvement is also assessed during surgery to determine if the tumour has spread which requires dissection of the axilla and adjuvant treatment. The prognostic and treatment predictive factors assessing the nature of the tumour are all routinely based on the status of the primary tumour.
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| 9. |
- Liljedahl, Leena, et al.
(author)
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Targeted Selected Reaction Monitoring Verifies Histology Specific Peptide Signatures in Epithelial Ovarian Cancer
- 2021
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In: Cancers. - : MDPI AG. - 2072-6694. ; 13:22
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Journal article (peer-reviewed)abstract
- Simple SummaryOvarian cancer is a lethal disease due to its late phase discovery. Any steps towards improving early diagnostics will dramatically increase survival rates. To identify new ovarian cancer biomarker panels, we need to focus on early-stage disease and all histologic subtypes. In this study we have, based on prior discoveries, constructed a multiplexed targeted selected-reaction-monitoring assay to detect peptides from 177 proteins in only 20 mu L of plasma. The assay was evaluated in patients with a focus on early-stages and all ovarian cancer histologies in separate groups. With multivariate analysis, we found the highest predictive value in the benign vs. low-grade serous (Q2 = 0.615) and mucinous (Q2 = 0.611) early stage compared to all malignant (Q2 = 0.226) or late stage (Q2 = 0.43) ovarian cancers. The results show that each ovarian cancer histology subgroup can be identified by a unique panel of proteins.Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I-II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 mu L of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.
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| 10. |
- Marcisauskas, Simonas, 1988, et al.
(author)
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Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2
- 2019
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In: Journal of Proteomics. - : Elsevier BV. - 1874-3919 .- 1876-7737. ; 196, s. 57-68
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Journal article (peer-reviewed)abstract
- Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT–LC–MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p <.05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. Significance: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.
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