Biology subjects

Chen, A. A.

Publications and source records attributed to Chen, A. A..

23 records · Page 2Linked to original sources

bioRxiv2021

The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution

Cutaneous melanoma is a highly immunogenic malignancy, surgically curable at early stages, but life- threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1 mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen. STATEMENT OF SIGNIFICANCEThe reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classical histopathology, spatial profiling of proteins and mRNA reveals recurrent morphological and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact.

cancer biology↗

bioRxiv2021

Micro-region transcriptomics of fixed human tissue using Pick-Seq

Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology and disease. Recently developed spatial transcriptomic methods use fresh-frozen specimens but most diagnostic specimens, clinical trials, and tissue archives rely on formaldehyde-fixed tissue. Here we describe the Pick-Seq method for deep spatial transcriptional profiling of fixed tissue. Pick-Seq is a form of micro-region sequencing in which small regions of tissue, containing 5-20 cells, are mechanically isolated on a microscope and then sequenced. We demonstrate the use of Pick-Seq with several different fixed and frozen human specimens. Application of Pick-Seq to a human melanoma with complex histology reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immuno-editing. Parallel imaging confirms changes in immuno-phenotypes and cancer cell states. This work demonstrates the ability of Pick-Seq to generate deep spatial transcriptomic data from fixed and archival tissue with multiplexed imaging in parallel.

cancer biology↗

bioRxiv2020

Statistical Pitfalls in Brain Age Analyses

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap". Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gaps dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap (MBAG) is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality. HighlightsO_LIThe brain age gap is an out-of-sample residual, and as such varies as a function of age. C_LIO_LIA recently proposed modification of the brain age gap, designed to mitigate the dependence on age, results in inflated model accuracy statistics if used incorrectly. C_LIO_LIGiven these limitations, we suggest that new methods should be developed to quantify deviation from normal developmental and aging trajectories. C_LI

neuroscience↗

bioRxiv2019

Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing scanner-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of multivariate pattern analysis (MVPA) has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing scanner effects in mean and variance may not be sufficient for MVPA. This stems from the fact that such methods fail to address how correlations between measurements can vary across scanners. Data from the Alzheimers Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across scanners and that popular harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization.

neuroscience↗

bioRxiv2018

Computationally Reconstructing Cotranscriptional RNA Folding Pathways from Experimental Data Reveals Rearrangement of Non-Native Folding Intermediates

The series of RNA folding events that occur during transcription, or a cotranscriptional folding pathway, can critically influence the functional roles of RNA in the cell. Here we present a method, Reconstructing RNA Dynamics from Data (R2D2), to uncover details of cotranscriptional folding pathways by predicting RNA secondary and tertiary structures from cotranscriptional SHAPE-Seq data. We applied R2D2 to the folding of the Escherichia coli Signal Recognition Particle (SRP) RNA sequence and show that this sequence undergoes folding through non-native intermediate structures that require significant structural rearrangement before reaching the functional native structure. Secondary structure folding pathway predictions and all-atom molecular dynamics simulations of folding intermediates suggest that this rearrangement can proceed through a toehold mediated strand displacement mechanism, which can be disrupted and rescued with point mutations. These results demonstrate that even RNAs with simple functional folds can undergo complex folding processes during synthesis, and that small variations in their sequence can drastically affect their cotranscriptional folding pathways.\n\nHighlights- Computational methods predict RNA structures from cotranscriptional SHAPE-Seq data\n- The E. coli SRP RNA folds into non-native structural intermediates cotranscriptionally\n- These structures rearrange dynamically to form an extended functional fold\n- Point mutations can disrupt and rescue cotranscriptional RNA folding pathways

biochemistry↗