Journal: Optics Express

Article Title: Fabrication of glass microlenses using focused Xe beam

doi: 10.1364/oe.26.013647

Figure Lengend Snippet: Fig. 2. Scanning electron microscopy (SEM) images of a) overview of different microlenses milled into the glass substrate using P-FIB at 60 and 200 nA of Xe beam current and b) cross- section view revealing the profile of a 55-µm microlens. SEM stage tilt is 52°. c) Parabolic fit to the measured profile of the lens from (b).

Article Snippet: M. B. Stern and T. R. Jay, “Dry-Etching for Coherent Refractive Microlens Arrays,” J Opt a-Pure Appl Op 8, S407– S429 (1994).

Techniques: Electron Microscopy

Journal: Optics Express

Article Title: Fabrication of glass microlenses using focused Xe beam

doi: 10.1364/oe.26.013647

Figure Lengend Snippet: Fig. 1. Pattern for milling microlens in glass with P-FIB is a) a parabolic profile where lateral pixels represent the position of the beam and the value of the pixels is proportional to the amount of material to be milled away. b) The patterns are 8-bit bitmap images. To reduce digitalization errors, the pattern is raster-scanned in three steps with each step the pattern rotated by 120°. The procedure is repeated until the desired mill depth is achieved.

Article Snippet: M. B. Stern and T. R. Jay, “Dry-Etching for Coherent Refractive Microlens Arrays,” J Opt a-Pure Appl Op 8, S407– S429 (1994).

Techniques:

Journal: Optics Express

Article Title: Fabrication of glass microlenses using focused Xe beam

doi: 10.1364/oe.26.013647

Figure Lengend Snippet: Fig. 4. Focusing properties characterization of microlenses by mapping the intensity at different planes around the focus (a-c, a microlens with a sag height 7.3 µm) (d,e,f,g). Cross- sectional view through the focused beam for microlenses with 7.3 µm, 9 µm, 13.2 µm and 1.8 µm sag height, respectively. Some misalignments in the figures are due to the mechanical drift and vibrations. h) Comparison of intensity profiles at the focus of the microlenses with the corresponding Airy function fit.

Article Snippet: M. B. Stern and T. R. Jay, “Dry-Etching for Coherent Refractive Microlens Arrays,” J Opt a-Pure Appl Op 8, S407– S429 (1994).

Techniques: Comparison

Journal: PLoS ONE

Article Title: Proteomic fingerprinting in HIV/HCV co-infection reveals serum biomarkers for the diagnosis of fibrosis staging

doi: 10.1371/journal.pone.0195148

Figure Lengend Snippet: SELDI-TOF MS biomarkers detected in HIV/HCV co-infection.

Article Snippet: Samples were randomized within and across arrays with blank spots included as negative controls and applied to three types of ProteinChip arrays: weak cation exchange (CM10), immobilized metal affinity capture (IMAC30) and hydrophobic/reverse-phase (H50) (all from Bio-Rad).

Techniques:

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Schematic overview of the generative process used to produce artificial spatial data. 1) First a set of seeding cells (red and blue circles) are placed in a defined tissue domain (square), every seeding cell hosts one unique copy number event. 2) The cells are allowed to “grow” within the tissue domain until the number of cells in the domain exceeds a predetermined number. 3) Mutations in the genome occur stochastically during growth and as a result, subpopulations (indicated by colour) of cells with similar genomic profiles arise. 4) Unoccupied space in the tissue domain is filled with benign cells (no copy number variations), spatial capture locations are placed in a grid over the grown tissue and transcripts are “captured” from the cells overlying each spot. 5) Synthetic spatial expression data is produced together with associated ground truth genomic data (both on spot and cell level). b , Results from applying siCNV (bottom) to a set of synthetic data together with ground truth information (top), only cells residing at spots being annotated as non-benign are shown. Blue indicates a deletion event while red indicates an amplification event. The ground truth shows the genomic profiles for all cells contributing to the spots assigned to a given clone. Comparing the inferred state with the ground truth on a clone 19 level, the average accuracy across genes was 0.90 (standard deviation 0.10) c , Spatial organization of the synthetic data analysed in (b), with thumbnail of the complete cell population in the artificial tissue, each pixel corresponding to a cell. The cells’ intensity levels are proportional to their total number of associated copy number events. Circles represent the spots used to “capture” transcripts. Spots are coloured by their inferred clone identity. Note how Clone 2, predicted to have zero copy number events, is found along the borders of both foci, where there’s a mixture of benign and non-benign cells. d , siCNV outputs from simulated synthetic data of spots simulating ST 1k array (low-resolution) with 100 μm spot diameter and centre-to-centre distance of 200 μm. e , Visium (high-resolution). High resolution spots were 0.55x size of low resolution and had 5x more spots per area. The synthetic ground truth data were identical for both.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Expressing, Produced, Amplification, Standard Deviation

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , UMAP summary of GEFs from 1k spatial transcriptomics experiments of prostate samples from patient 1. b , UMAP summary of GEFs from high resolution Visium experiments of prostate samples from patient 1. Top marker genes for each GEF are available in Supplementary Table , . c , Benign GEFs from b (high resolution) were used as a reference set for analysis of d , Tumour GEFS from b (high resolution). e , Snapshot of inferCNV profiles for chr 7 and 8 from GEF10. GEF inferCNV heterogeneity is highlighted by 3 subclones: the first harbouring no changes to chr 7 and 8, the second having a deletion and amplification in chr 8, and the last having alterations in both chr 7 and chr 8. While further subclustering of GEF10 spots using gene expression factors improved GEF to clone concordance, GEF to clone heterogeneity remained. f , Tumor GEFs distribution by siCNV clones (Fig. ). GEF = Gene Expression Factor, chr = Chromosome, siCNV = spatial inferCNV.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Marker, Amplification, Gene Expression, Clone Assay

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Visual selection of benign epithelial spots harbouring the least amount of inferred copy number variations, as outlined by the black box bounding box. Arrows identify dendrogram nodes corresponding to barcoded spots within the box. b , InferCNV output of the dendrogram nodes with numerical identifiers for selection corresponding to Panel a. c , Finalized benign reference set from analysis of epithelial cells in prostate patient 1, section H2_1 (Fig. ). d , Global spatial inferCNV profiles of the selected benign reference set from panel a, the remainder of the benign not included in the reference set, altered benign (Clone C, Fig. ), and the other Visium spots with luminal epithelial annotations (PIN, GG1, GG2, GG4, GG4 Cribriform).

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Selection

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Genome-wide derived analysis (siCNVs) for all Visium spots harbouring tumour from prostate patient 1. Clonal groupings of spots (with approximately 10–15 cells each) were determined by hierarchical clustering. Chr., chromosome. b , Phylogenetic clone tree of the tumour clones from a , with grey clones representing unobserved, inferred common ancestors. Clone circle area is proportional to the number of spots and branch length was determined by weighted quantity of CNVs (both on a logarithmic scale). siCNV changes for each clone are available in Supplementary Table . c , Representation of all tissue sections from prostate patient 1. Thicker black lines denote original boundaries annotated by initial clinical pathology. d , Consensus epithelial histological annotations for sections H1_4, H1_5 and H2_5, corresponding to the right tumour focus. e , Spatial visualization of tumour clones (from a ). The dashed lines mark areas where no spatial transcriptomics data were obtained owing to these regions being outside of barcoded array surfaces.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Genome Wide, Derivative Assay, Clone Assay

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Consensus pathology annotations for tumour spots from sections H2_1, H2_2, and H1_2. b , Clonal groupings of spots (approx. 10-15 cells each) determined by hierarchical clustering. c , Distinct siCNV profile of GG1 tumour focus from organscale prostate patient 1. siCNV profiling of epithelial Visium spots from section H1_2. d , Spot level histology and siCNV clone calls. GG = ISUP Gleason ‘Grade Group’, siCNV = spatial inferCNV.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques:

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Somatic WGS CNV profile of patient 1 diagnosed with medulloblastoma (grade IV, desmoplastic/nodular, SHH-activated) with b , match normal blood. c , Somatic WGS CNV profile of Chr 2, 3 and 9 of patient 2 diagnosed with medulloblastoma (grade IV, classic morphology, SHH-activated) with d , match normal blood. Notably inferCNV analysis on Visium data did not show any genomic variability in chr 2 but since Visium and WGS data were generated from different locations of each tumour, we speculate that the observed WGS CNV patterns in patient 2 could be due to the inherent spatial heterogeneity of DNA copy number alterations observed by others when sampling multiple sites of medulloblastoma tumours. e , Somatic WGS CNV profile of Chr 2, 3 and 9 of patient 3 diagnosed with CNS embryonal tumour (grade IV, multi-layered rosettes, NOS) with d , match normal blood. No CNV was detected by WGS in the chromosomes not displayed. WGS = Whole-genome sequencing. Chr = Chromosome. SHH = Sonic hedgehog. CNS = Central nervous system. NOS = Not otherwise specified.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Generated, Sampling, Sequencing