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Biomedical subjects

Matthew McAuliffe

Publications and source records attributed to Matthew McAuliffe.

2 recordsLinked to original sources

Novel lymphotoxin alpha (LTalpha) knockout mice with unperturbed tumor necrosis factor expression: reassessing LTalpha biological functions.

Lymphotoxin alpha (LTalpha) can exist in soluble form and exert tumor necrosis factor (TNF)-like activity through TNF receptors. Based on the phenotypes of knockout (KO) mice, the physiological functions of LTalpha and TNF are considered partly redundant, in particular, in supporting the microarchitecture of the spleen and in host defense. We exploited Cre-LoxP technology to generate a novel neomycin resistance gene (neo) cassette-free LTalpha-deficient mouse strain (neo-free LTalpha KO [LTalphaDelta/Delta]). Unlike the "conventional" LTalpha-/- mice, new LTalphaDelta/Delta animals were capable of producing normal levels of systemic TNF upon lipopolysaccharide (LPS) challenge and were susceptible to LPS/D-galactosamine (D-GalN) toxicity. Activated neutrophils, monocytes, and macrophages from LTalphaDelta/Delta mice expressed TNF normally at both the mRNA and protein levels as opposed to conventional LTalpha KO mice, which showed substantial decreases in TNF. Additionally, the spleens of the neo-free LTalpha KO mice displayed several features resembling those of LTbeta KO mice rather than conventional LTalpha KO animals. The phenotype of the new LTalphaDelta/Delta mice indicates that LTalpha plays a smaller role in lymphoid organ maintenance than previously thought and has no direct role in the regulation of TNF expression.

Animals↗

Whole cell segmentation in solid tissue sections.

BACKGROUND: Understanding the cellular and molecular basis of tissue development and function requires analysis of individual cells while in their tissue context. METHODS: We developed software to find the optimum border around each cell (segmentation) from two-dimensional microscopic images of intact tissue. Samples were labeled with a fluorescent cell surface marker so that cell borders were brighter than elsewhere. The optimum border around each cell was defined as the border with an average intensity per unit length greater that any other possible border around that cell, and was calculated using the gray-weighted distance transform. Algorithm initiation requiring the user to mark two points per cell, one approximately in the center and the other on the border, ensured virtually 100% correct segmentation. Thereafter segmentation was automatic. RESULTS: The method was highly robust, because intermittent labeling of the cell borders, diffuse borders, and spurious signals away from the border do not significantly affect the optimum path. Computer-generated cells with increasing levels of added noise showed that the approach was accurate provided the cell could be detected visually. CONCLUSIONS: We have developed a highly robust algorithm for segmenting images of surface-labeled cells, enabling accurate and quantitative analysis of individual cells in tissue.

Algorithms↗