Data Mining for Research and Diagnostics
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1 UCL DEPARTMENT OF GEOGRAPHY UCL INSTITUTE OF NEUROLOGY Data Mining for Research and Diagnostics Sebastian Brandner, UCL Institute of Neurology, London
2 Data mining for Research and Diagnostics Our setup Establishing a rapid throughput image analysis pathway for tissue-based molecular diagnostics Analysis of morphological features and immunohistochemical markers Muscle morphometry Peripheral nerve macrophages in preclinical tretment Autopsy morphometry in prion disease Experimental model of brain tumours
3 Specimen preparation and staining Surgical removal of tissue Experimental tissues Tissue processing in PathLab Preparation of histological slides Diagnostic decision making Choice of test Automated IHC, CISH or SISH QC: Visual inspection of slide h 4 h 8h 10 min
4 Digitising and automated image analysis Slides Slide scanning File archiving Web based slide management Submission for image analysis Image analysis Data output to user
5 Detection and quantification of chromosomal losses in SISH slides Aim: to develop a fast-track image analysis to detect SISH signals on FFPE tissue Procedure: Use established (HER2) control slides from ROCHE Setting up image analysis algorithm in Definiens Tissue Studio and Developer Validating image analysis algorithm
6 Normal cell Unbalanced Translocation LOH 1p/19q 36 1p der(1;19)(p10;q10). 19p q 24 1q
7 Definiens Developer and Tissue Studio Identification of region of interest (Ventana control slide; HER2) Regions of interest in detail
8 High magnification of signal within ROI
9 Definiens Developer and Tissue Studio First nuclear identification
10 Nuclear segmentation
11 Result: all nuclei are identified and segmented within the ROI
12 Spot identification (ISH signal) Red= red signal, blue= silver signal
13 Representation of ISH signal on sectioned nuclei 2.5μm ~10μm
14 Validation step: identification and visualisation of red and silver spots code Red Silver
15 Result: Segmentation and spot quantification
16 Data output: counts of red and silver spots in test slide Feature Value Red Silver No Spots Red 1 Silver Red 1 Silver Red 2 Silver Red 2 Silver Red spots (total) 4713 Silver spots (total)
17 Conclusion: image analysis of ISH signal Accurate determination of ISH spot count in tissue sections 1000 s of nuclei counted in minutes (5000 in our ROI) Superior to manual counting of FISH nuclei (usually 100)
18 Examples: Analysis of morphological and immunohistochemical markers across multiple samples Muscle morphometry: Establishing a formula for muscle fibre diameter measurement Peripheral Nerve Macrophage quantification Autopsy morphometry Quantification and overlay of Spongiosis, gliosis and prion protein in CJD brains. Brain tumours: Quantification of expression profiles and comparison with cdna microarrays
19 Example 1: Development of a formula in Definiens Developer to determine muscle fibre diameter
20 Threshold selection from the light area bordering the edge of the scene Use spectrin IHC to identify muscle fibres Tissue identification homogeneity and context to reclassify light areas within the tissue and dark areas within the background Tissue identification II Fill and clean up background
21 Segmenting the Tissue A series of methods are used distance mapping watershed segmentation split connected fibres
22 Cleaning Up Fibre Selection Finally any potential fibres that do not meet set criteria are excluded: Too Small (<20µm) Too Large (2x mean fibre size) Elongated (l/w > 4) 5% error (top and bottom 2.5%) A A-B A-C A-D Minor Axis B C D Major Axis Cylindric sections of increasing eccentricity
23 Output and further data analysis Histogram (10µm bins) of muscle fibre widths
24 Example of muscle fibre analysis in an experimental model Exercise Septic insult D2 analysis D7 analysis D14 analysis Day 2 Day 14
25 Identification of subtle shift of fibre diameter
26 Power of Image Analysis Population Size possible to analyse large areas Detail the accuracy and confidence in results Subtle differences detected Adaptation of markers to capabilities of software can simplify analysis and improve accuracy Once established can be used for large sample series pays off time consuming development
27 Example 2: unsupervised analysis of endoneurial inflammation in preclinical drug tests Analysis of inflammatory response in sciatic nerves of mice: Iba1 IHC on longitudinal sections Treatment and control group Detection of marker (=endoneurial macrophages) Size of marker Frequency and coverage
28 Image analysis of macrophages in sciatic nerves of mice
29 Average Cell Size (µm²) Macrophage quantification in whole mount mouse sciatic nerves: unsupervised analysis 30 Microglia in Sciatic Nerves PRN100 Treatment 4mg group PBS group Microglia Coverage in Nerve (%)
30 Power of Image Analysis Unbiased assessment analysis blind to treatment group Reliable Identification of marker accuracy and confidence in results Simple algorithm can be applied to large sample series
31 Example 3: Standardise tissue analysis on brain autopsy tissue Example: Prion disease (Creutzfeldt Jakob Disease) Several subtypes exist, and show a variation in the pathological patterns Vacuolation (spongiosis) on H&E Gliosis (astrocyte reaction) GFAP IHC Prion protein, detected by IHC
32 % Coverage % Coverage % Coverage UCL INSTITUTE OF NEUROLOGY NP10-05 ICSM: Moderate Diffuse & Ribbon 0 1 NP10-35 ICSM: Mild-Moderate, Patchy NP10-48 ICSM: Ribbon 0 1 High Medium Low
33 Stain Coverage (%) GFAP Prion protein Vacuoles Pial surface Grey-white matter junction Distance (%) GFAP PrP Vacuoles Ribbon GFAP PrP Vacuoles Vacuoles per mm 2
34 Stain Coverage (%) Stain Coverage (%) Distance (%) GFAP PrP Vacuoles Mild Vacuolation GFAP PrP Vacuoles Vacuoles per mm 2 Distance (%) GFAP PrP Vacuoles Severe Vacuolation GFAP PrP Vacuoles Vacuoles per mm 2
35 UCL DEPARTMENT OF GEOGRAPHY UCL INSTITUTE OF NEUROLOGY Example 4: expression analysis of stemnessand tumour markers in experimental brain tumours Expression profiles of mouse brain tumour models identify similarities between murine and subsets of human gliomas
36 Our Model system Mouse with multiple floxed genes Rb/p53 PTEN/p53 Rb/p53/PTEN Rb/PTEN Intraventricular Adeno cre Derivation & culture of neurospheres +Adeno-Cre Intrinsic ( primary ) brain tumour Transplantation of recombined tumour-spheres ( secondary tumours )
37 PNET Rb/p53 Rb/p53/PTEN p53/pten Rb/PTEN Glioma (OA) GBM Intraventricular cre: Primary tumours PNET (GLIOMA) PNET (GLIOMA) GLIOMA ONLY No Tumour In vitro cre + transplanted tumour spheres: secondary PNET+ (GLIOMA) PNET+ GLIOMA GLIOMA ONLY GBM
38 Brain Tumour Analysis: Histology Objective for analysis Compare marker expression between mouse tumours generated in different genetic and experimental settings Differentiate between nuclear and cytoplasmic stain Differentiate between intensity levels and density of positive cells
39 Pten/p53: Oligodendroglial tumours (Anaplastic oligoastrocytoma) Stemness Transient amplifying Neural GFAP PDGFRα Nestin Sox2 Olig2 DCX MAP2 SYN NeuN
40 Pten/p53: Oligodendroglial tumours (Anaplastic oligoastrocytoma) Stemness Transient amplifying Neural GFAP PDGFRα Nestin Sox2 Olig2 DCX MAP2 SYN NeuN
41 ROI Classification Automatic /Trained Whole Sample Manual Select Regions Tissue Empty Areas
42 Identification of Cellular Components Nuclear Cytoplasm Background Hematoxylin Low Medium High BC Hematoxylin Low Medium BC High
43 Analysis and overview of Data The % area coverage and % total number of nuclei were calculated for cytoplasmic and nuclear markers, respectively. Data was compiled to give a heat map with red indicating high, white middle and blue low expression. Genotype Tissue Type n Histological diagnosis GFAP Pdgfra Nestin Sox2 Olig2 Dcx CD15 MAP2 Syn NeuN Primary Tumour (SVZ-cre) 2 Oligo-Astrocytoma p53/pten In vitro recombined spheres 3 Tumour Spheres Tumour graft 2 Oligo-Astrocytoma Primary Tumour (SVZ-cre) 6 PNET In vitro recombined spheres 2 Tumour Spheres p53/pten/rb Tumour graft glial 2 Oligo-Astrocytoma Tumour graft PNET 2 PNET Primary Tumour (SVZ-cre) 3 PNET Stain intensity % coverage Low Medium High p53/rb Pten/Rb In vitro recombined spheres 4 Tumour Spheres Tumour graft glial 2 Oligo-Astrocytoma Tumour graft PNET 2 PNET Primary Tumour (SVZ-cre) 0 No Tumours In vitro recombined spheres 3 Tumour Spheres Tumour graft 3 Glioblastoma Non rec Control Neurospheres 3 Neurospheres
44 Exon arrays expression profile of tumours, in vitro spheres and tumour grafts PNET GBM Oligoastro Spheres NB Unsupervised hierarchical clustering of whole-genome expression profiles Tumours separate from each other based on histological appearance, not genotype. Tumours separate from cultured lines (spheres) and normal brain. Spheres do not systematically separate into clusters based on genotype.
45 Publication with image analysis data Genotype Tissue Type n Histological diagnosis GFAP Pdgfr NestinSox2 Olig2 Dcx CD15 MAP2 Syn NeuN a Primary Tumour (SVZ-cre) 2 Oligo-Astrocytoma p53/pten In vitro recombined spheres 3 Tumour Spheres Tumour graft 2 Oligo-Astrocytoma Primary Tumour (SVZ-cre) 6 PNET In vitro recombined spheres p53/pten/rb 2 Tumour Spheres Tumour graft glial 2 Oligo-Astrocytoma Tumour graft PNET 2 PNET Primary Tumour (SVZ-cre) 3 PNET p53/rb In vitro recombined spheres 4 Tumour Spheres Tumour graft glial 2 Oligo-Astrocytoma % Low Medium High Pten/Rb Tumour graft PNET 2 PNET Primary Tumour (SVZ-cre) 0 No Tumours In vitro recombined spheres 3 Tumour Spheres Tumour graft 3 Glioblastoma Non rec Control Neurospheres 3 Neurospheres
46 Summary Digital image analysis for diagnostic tests and for research High accuracy Unbiased analysis of vast numbers of items (cells, fibres, nuclei, etc) and features (nuclei, distance, shape, relationships) Correlation with mrna expression
47 Division of Neuropathology UCL-IoN Matthew Ellis: Implementation of all tissue analysis algorithms and implementation of digital imaging workflow. Complete analysis of muscle, nerve and tumours UCL IQPath team Angela Richard Loendt (lead) with Tamsin Wilkins, Francesca Launchbury and Jessica Broni) Preparation of histological slides, slides scanning and data management Definiens, Munich Tamara Haeberlin; Bjorn Reiss, Jan Schoblocher Roche diagnostics (HER slides) Alex McDonald
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