Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M.; Hostetter, Galen; Winter, Jordan M.; Cherba, David; Brand, Randall E.; Allen, Peter J.; Sempere, Lorenzo F.; Haab, Brian B.

doi:10.1021/acs.analchem.5b03159

Cited by 21 publications

(18 citation statements)

References 34 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is important for image segmentation. A lot of research is devoted to the problem of choosing a threshold [9,15,16]. But as practice shows, the problem of choosing a threshold arises again.…”

Section: Discussionmentioning

confidence: 99%

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Babker

Lyashenko

2018

IJCBR

View full text Add to dashboard Cite

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Babker

Lyashenko

2018

IJCBR

View full text Add to dashboard Cite

show abstract

“…The fluorescence images were quantified and analyzed using custom, in-house software 28 . (see the Supporting Information for additional details).…”

Section: Methodsmentioning

confidence: 99%

Characterizing Protein Glycosylation through On-Chip Glycan Modification and Probing

et al. 2016

Self Cite

View full text Add to dashboard Cite

Glycans are critical to protein biology and are useful as disease biomarkers. Many studies of glycans rely on clinical specimens, but the low amount of sample available for some specimens limits the experimental options. Here we present a method to obtain information about protein glycosylation using a minimal amount of protein. We treat proteins that were captured or directly spotted in small microarrays (2.2 × 2.2 mm) with exoglycosidases to successively expose underlying features, and then we probe the native or exposed features using a panel of lectins or glycan-binding reagents. We developed an algorithm to interpret the data and provide predictions about the glycan motifs that are present in the sample. We demonstrated the efficacy of the method to characterize differences between glycoproteins in their sialic acid linkages and N-linked glycan branching, and we validated the assignments by comparing results from mass spectrometry and chromatography. The amount of protein used on-chip was about 11 nanograms. The method also proved effective for analyzing the glycosylation of a cancer biomarker in human plasma, MUC5AC, using only 20 μL of the plasma. A glycan on MUC5AC that is associated with cancer had mostly 2,3 linked sialic acid, whereas other glycans on MUC5AC had a 2,6 linkage of sialic acid. The on-chip glycan modification and probing (on-chip GMAP) method provides a platform for analyzing protein glycosylation in clinical specimens and could complement the existing toolkit for studying glycosylation in disease.

show abstract

“…The resulting images were quantified and analyzed using custom, in-house software [32] that locates the spots and quantifies their intensities, subtracts the local background from the median intensity of each spot, and removes any outliers from the six-replicate spots. To remove outliers, the program calculates the Grubbs’ statistic for the spot farthest from the mean of the replicates, rejects the spot if the Grubbs’ statistic exceeds a preset threshold (here using p < 0.1), and repeatedly removes spots until no outliers remain or to a minimum of three spots.…”

Section: Methodsmentioning

confidence: 99%

A Gastric Glycoform of MUC5AC Is a Biomarker of Mucinous Cysts of the Pancreas

et al. 2016

Self Cite

View full text Add to dashboard Cite

Molecular indicators to specify the risk posed by a pancreatic cyst would benefit patients. Previously we showed that most cancer-precursor cysts, termed mucinous cysts, produce abnormal glycoforms of the proteins MUC5AC and endorepellin. Here we sought to validate the glycoforms as a biomarker of mucinous cysts and to specify the oligosaccharide linkages that characterize MUC5AC. We hypothesized that mucinous cysts secrete MUC5AC displaying terminal N-acetylglucosamine (GlcNAc) in either alpha or beta linkage. We used antibody-lectin sandwich assays to detect glycoforms of MUC5AC and endorepellin in cyst fluid samples from three independent cohorts of 49, 32, and 66 patients, and we used monoclonal antibodies to test for terminal, alpha-linked GlcNAc and the enzyme that produces it. A biomarker panel comprising the previously-identified glycoforms of MUC5AC and endorepellin gave 96%, 96%, and 87% accuracy for identifying mucinous cysts in the three cohorts with an average sensitivity of 92% and an average specificity of 94%. Glycan analysis showed that MUC5AC produced by a subset of mucinous cysts displays terminal alpha-GlcNAc, a motif expressed in stomach glands. The alpha-linked glycoform of MUC5AC was unique to intraductal papillary mucinous neoplasms (IPMN), whereas terminal beta-linked GlcNAc was increased in both IPMNs and mucinous cystic neoplasms (MCN). The enzyme that synthesizes alpha-GlcNAc, A4GNT, was expressed in the epithelia of mucinous cysts that expressed alpha-GlcNAc, especially in regions with high-grade dysplasia. Thus IPMNs secrete a gastric glycoform of MUC5AC that displays terminal alpha-GlcNAc, and the combined alpha-GlcNAc and beta-GlcNAc glycoforms form an accurate biomarker of mucinous cysts.

show abstract

Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

Cited by 21 publications

References 34 publications

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Characterizing Protein Glycosylation through On-Chip Glycan Modification and Probing

A Gastric Glycoform of MUC5AC Is a Biomarker of Mucinous Cysts of the Pancreas

Contact Info

Product

Resources

About