A guide on the underlying physics, prevention and removal strategies of undesirable bubbles in microfluidics.

We have developed am ethod for spatially resolved genetic analysis of formalin-fixed paraffin-embedded (FFPE) cell blocka nd tissue sections.T his method involves local sampling using hydrodynamic flow confinement of al ysis buffer,f ollowed by electrokinetic purification of nucleic acids from the sampled lysate.W ec haracterized the method by locally sampling an arrayo fp oints with ac irca 200 mm diameter footprint, enabling the detection of single KRAS and BRAF point mutations in small populations of RKOa nd MCF-7 FFPE cell blocks.T oi llustrate the utility of this approach for genetic analysis,w ed emonstrate spatially resolved genotyping of FFPE sections of human breast invasive ductal carcinoma.

We have developed a method for spatially resolved genetic analysis of formalin‐fixed paraffin‐embedded (FFPE) cell block and tissue sections. This method involves local sampling using hydrodynamic flow confinement of a lysis buffer, followed by electrokinetic purification of nucleic acids from the sampled lysate. We characterized the method by locally sampling an array of points with a circa 200 μm diameter footprint, enabling the detection of single KRAS and BRAF point mutations in small populations of RKO and MCF‐7 FFPE cell blocks. To illustrate the utility of this approach for genetic analysis, we demonstrate spatially resolved genotyping of FFPE sections of human breast invasive ductal carcinoma.

This heterogeneity often manifests uncontrollably in diseased states at the molecular (genomic and transcriptomic) and the phenotypic level, making progression difficult to predict. Specifically in cancer, spatial cellular heterogeneity has been ascribed to drive tumor development, progression, and treatment resistance. [1][2][3] The last decades have seen tremendous growth in understanding the effect of driving agents (genetic, epigenetic, or external factors) on tumor progression and evolution, with single cell studies [4][5][6] providing a considerable leap in the understanding of tumor heterogeneity. These drivers however manifest at different scales, where a few cells to a whole subpopulation and its microenvironment can act in concert to drive progression, making the context of their spatial location critical in defining their interactions. Context-aware analysis requires an integrated strategy for high spatial resolution recovery of user-defined microscale areas from tissue sections for a) customizable molecular analysis, and b) high resolution molecular profiling of biomolecules that enable biomarker discovery. Current approaches in cancer research utilize analysis of monoclonal cell cultures, tumor models and bulk biopsies, to elucidate roles of biomolecules in cancer progression. These target biomarkers are then used by medical practitioners to execute tests in order to provide a prognosis for disease progression, diagnosis for classification, and prediction of the classified tumor's response to therapy. These processes, however, contain sparse or only rudimentary spatial information and there is a growing need within the research community to elucidate the impact of spatial heterogeneity for clinical evaluation. This necessitates the development of methods for spatial genomic and transcriptomic detection, which can be easily made accessible to the broader cancer research community.Current methods to characterize tumor tissues spatially are mainly based on in situ techniques, visually providing a spatial map of a biomolecule of interest. Traditional in situ approaches (immunohistochemistry, DNA fluorescence in situ

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