PubMed · 8337043
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M Kaplan, J Z Glustein. 1993. Artifacts causing artifacts.. https://pubmed.ncbi.nlm.nih.gov/8337043/
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MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.
By first separating mutant from nonmutant DNA sequences on the basis of their melting temperatures and then increasing the number of copies by high-fidelity DNA amplification, we have developed a method that allows observation of point mutations in biological samples at fractions at or above 10-6. Using this method, we have observed the hotspot point mutations that lie in 100 base pairs of the mitochondrial genome in samples of cultured cells and human tissues. To date, 19 mutants have been isolated, their fractions ranging from 4x10-4 down to the limit of detection. We performed specific tests to determine if the observed signals were artefacts arising from contamination, polymerase errors during PCR or DNA adducts created during the procedure. We also tested the possibilities that DNA replication mismatch intermediates, or endogenous DNA adducts that were originally present in the cells, were included with true mutants in our separation steps and converted to mutants during PCR. We show that while most of the mutants behave as double-stranded point mutants in the cells, some appear to arise at least in part from mismatch intermediates or cellular DNA adducts. This technology is therefore sufficient for the observation of the spectrum of point mutations in human mitochondrial DNA and is a tool for discovering the primary causes of these mutations.
Human somatic cells have essentially no telomerase activity. Telomerase is linked to tumor genesis and is a valuable marker for malignant growth. Extreme paucity of the enzyme neccessitated development of a PCR-based assay, 'telomeric repeat amplification protocol' (TRAP). Unfortunately, this method is not without difficulties. Amplification products are not related to the size of the amplified telomerase products. Furthermore, false positive results can occur, and careful control of reaction conditions is crucial. We analyzed in detail the molecular basis of artifacts. Based on these data, reverse PCR primer was changed and both problems in the TRAP assay were eliminated.