in A673V in A673T Superoxide dismutase P04179 DOWN DOWN Destroys superoxide anion radicals X X X X Serum albumin P02768 DOWN DOWN promotes neuronal survival X X Flotillin-2 Q14254 DOWN DOWN negative regulation of amyloid precursor protein catabolic process X X X Tubulin alpha-8 chain Q9NY65 UP DOWN Translocation of GLUT4 to the plasma membrane, GAP junction assembly X X Transmembrane protein 35 Q53FP2 UP DOWN interact with NGFR and modulate neurite outgrowth X X AP-3 complex subunit delta-1 O14617 UP N.R. neurotransmitter receptor transport X X X Chloride transport protein P51797 UP N.R. voltage-gated chloride channel activity X X Pre-mRNA-processing factor 40 homolog A O75915 UP N.R. regulation of cell morphology and cytoskeletal organization X X X X Protein arginine N-methyltransferase 1 Q99873 UP N.R. transcriptional activity, neurite outgrowth X X X X X X Emerin P50402 UP N.R. Stabilizes and promotes the formation of a nuclear actin cortical network X X X X X X Eukaryotic translation initiation factor 5A- 1 P63241 UP N.R. mRNA-binding protein, brain development X X X X X Glutathione synthetase P48637 DOWN N.R. nervous system development X X X Neurosecretory protein VGF O15240 UP N.R. cell-cell interactions or in synatogenesis, neuropeptide activity X Nuclear pore membrane glycoprotein 210 Q8TEM1 DOWN UP nuclear pore assembly X X Sialic acid synthase Q9NR45 UP N.R. carbohydrate biosynthetic process X Actin-related protein 2/3 complex subunit 2 O15144 UP N.R. Actin polymerization X X GPI inositol-deacylase Q75T13 DOWN N.R. forebrain regionalization, protein transport X X cAMP-dependent protein kinase catalytic subunit beta P22694 UP UP activation of protein kinase A activity X X Signal recognition particle 19 kDa protein UP UP Signal-recognition-particle assembly X Dual specificity mitogen-activated protein kinase kinase 2 P36507;Q0275 0 UP UP activation of MAPK activity X X X Carboxypeptidase E P16870 UP DOWN metabolic processes, neuropeptide signaling pathway X X Leukocyte surface antigen CD47 Q08722 DOWN N.R. cell adhesion , integrins X X Cellular retinoic acid-binding protein 1/2 P29762, P29763 UP N.R. cellular development X X High mobility group protein HMG-I/HMG- Y P17096 DOWN N.R. chromatin binding X X Nuclear ubiquitous casein and cyclin- dependent kinase substrate 1 Q9H1E3 UP N.R. chromatin binding, regulation of insulin receptor signaling pathway X X Prefoldin subunit 5 Q99471 UP N.R. chaperone activity X X Small nuclear ribonucleoprotein Sm D1/D2 P62316, P62314 UP DOWN mRNA splicing X X Elongation factor Tu, mitochondrial P49411 UP N.R. protein biosynthesis X Eukaryotic translation initiation factor 3 subunit G O75821 UP N.R. initiation of protein synthesis X U4/U6 small nuclear ribonucleoprotein Prp3 O43395 UP N.R. pre-mRNA splicing X Name ID Role Synaptic plasticity Transcription regulated Membrane organization Gene splicing, expression Nuclear transport Axonal transport, cytoskeleton Metabolism (MAPK, PKA, Wnt) Oxidative stress / apoptosis 0 20 40 60 80 100 120 iCellDopa Neurons iCellGluta Neurons iCell Neurons Functional Phenotypic Similarity Frontal Cortex Midbrain/Cortex Hippocampus Spinal Cord Poisson i ia %sml r classifier FC Hc MB+FC SC Poisson FC 81 4.2 14 0.2 0.2 Hc 2.6 94 1 2.4 0 MB+FC 13 2 82 2.8 0 SC 1.2 1.6 2 94 0.2 Poisson 0 0 0 0 100 Genotype/Phenotype correlation in human iPSC-derived neuronal networks and in vitro HTS disease modeling with micro-electrode arrays coupled with transcriptome analyses. *1 2 1 Benjamin M. Bader , Maren Depke , Monica Segura-Castell , Maria 1 1 2 1 Winkler , Konstantin Jügelt , Frank Schmidt , Olaf H.-U. Schröder 1 NeuroProof GmbH, Rostock, Germany; contact: [email protected] 2 University Medicine Greifswald, Dept. of Functional Genomics, Greifswald, Germany Introduction To decrease attrition rates is one of the major challenges of drug discov- ery. One strategy is the development of more predictive pre-clinical in vitro models. Human induced pluripotent stem cell-derived (hiPSC) neuronal cultures promise higher physiological relevance and thus, better translation to the in vivo situation. In this context genetic cell models have been designed or produced from patient biopsies. However, one of the biggest concerns is their physiological relevance needed for disease mod- eling. Our aims were a) to identify a correlation between known disease geno- types (e.g. of familiar Alzheimer's disease) and their functional in vitro phe- notype and b) to better understand these human iPSC neuronal in vitro models by comparing them with primary mouse neuronal cell cultures. Methods Conclusions Primary culture: Primary mouse tissue cultures from embryos (NMRI) were cultured on MEAs for 4 weeks. hiPSC culture: We cultured human iPSC Neurons (Cellular Dynamics International, USA) on multi-well MEAs (Axion Biosystems) for up to 4 weeks. MEA recording: MEAs from Axion Biosystems, USA were recorded on the MAESTRO recording station. Per data point 60 minutes were recorded at 37°C und stable pH condition. MEA Data analysis: Acquired spike train data was analysed multi- parametrically yielding more than 200 parameters (NPWaveX software). Classification analyses were performed using PatternExpert software. Transcriptomic analyses: RNA quality control with Agilent Bianalyzer, whole genome Affymetrix GeneChip Human Transcriptome Array 2.0, Gene Ontology analysis. By comparing human functional phenotypes with those from different specific mouse brain regions we show that human neuronal cell exhibit specific and reproducible phenotypic similarity profiles (e.g. midbrain or hippocampus- like). Genetic manipulation affects these activity patterns thereby producing specific and significant functional phenotypes. For APP-mutated cells our transcriptome results showed regulation of more than 100 genes including genes involved in membrane plasticity, axonal trans- port, gene splicing, cell cycle, glutamate receptor signaling and neuro- protection. In conclusion, we show that hiPSC neurons are able to produce meaningful functional in vitro phenotypes, that these phenotypes can be changed by dis- ease-associated modulation and we provide first data for a genotype/ transcriptome/ phenotype correlation. Results Brain Region-Specific Cell Cultures with Unique Network Activity Patterns. Brain region-specific neuronal cell cultures from mice and a random spike train. Network spike train patterns of brain-region specific primary cell cultures derived from embryonic mouse tissue of frontal cortex (FC), spinal cord (SC, with dorsal root ganglia), hippocampus (Hc), and midbrain co- cultured with frontal cortex ( Mb+FC). Plotted are 60 s of 25 neurons of spontaneous network activity at 28 days in vitro. Frontal Cortex Hippocampus Spinal Cord Midbrain+Frontal Cortex Poisson (random spike train) Example MEA spike trains of iNeuron cell lines after 3-21 daysin vitro after thawing. Cultured on 48-well MEAs with 16 electrodes each. A high level of synchronization is shown by strong populations bursts for iCell Gluta and iCell Dopa Neurons. On 48 well MEAs a unit separation was performed yielding up to 32 active units on 16 electrodes per well. Cross validation shows that activity patterns are unique (high % self-recognition) and thus, also highly reproducible. Average values of 5 classification rounds with 100 data sets each using the combination of more than 200 parameters. Comparing phenotypes from human iPSC-neurons to primary neurons. Human iPSC-derived Neuronal Activity Patterns. Classification of activity patterns from three different human iPSC derived neurons during 1-4 weeks in culture shows a cell line specific phenotype (=fingerprint showing a distribution of similarities compared to primary mouse neuronal cultures). ICell Dopa Neurons show the highest similarity to ventral midbrain cultures mixed with cortex. ICell Gluta Neurons are most similar to primary hippocampus cultures and iCell Neurons are most similar to the poisson-generated in silico spike train which underlines their lower complexity. MyCellNeurons are differentiated with the iCellN protocol. These results highlight the importance to compare human with known reference data and show a) differences in quality of human iPSC neurons and b) physiological relevance. ICell Gluta Neurons at 21 div o ICell Neurons at 7 div 240 sec ICell Dopa Neurons at 21 div 300 sec 300 sec 300 sec Phenotypic Screening with MEA-Neurochips S ync hr on zatio i n Full disinhibition (with bicuculline, strychnine, NBQX) Native activity 30 s Oscillation 2 Burst Structure e.g. number, frequency and ISI of spikes in bursts; burst duration, amplitude, area, plateau position, plateau duration 1 General Activity e.g. spike rate, burst rate, burst period, percent of spikes in burst Read out: Extracellular action potentials on a single neuron and network activity level Spatio-temporal activity changes as well as synchronicity and oscillation in time scales of spikes and bursts ! ! Each specific spike train is described by 200 parameters in 4 categories: 3 Oscillation Variation over time as an indicator for the strength of the oscillation; in addition e.g. Gabor function parameters fitted to autocorrelograms 4 Synchronization Variation within the network as an indicator for the strength of the synchronization; in addition e.g. simplex synchronization, percent of units in synchronized burst Multiparametric Characterization of Neuronal Network Activity Neuronal Cell Culture Multichannel Recording Multiparametric Data Analysis Pattern Recognition Primary murine cell culture: - Frontal Cortex - Hippocampus - Midbrain/Cortex - Spinal Cord/DRG Neuronal human Stem Cells Network spike trains and single neuron action potential Over 200 descriptors at baseline and drug treatment - General activity - Synchronization - Oscillation - Burst structure Data base with functional fingerprints of over 100 basic and clinically compounds MAESTRO Recording System Axion Maestro MEA recording Station Neuronal network on electrode field NeuroProof Technology Burst Amplitude Burst Duration Burst Period Burst IBI Burst Plateau Burst Area Burst ISI Burst Duration burst Control activity Patient activity classification ! Comparison of activity from patient and control iPSC‘s 48 well MEA with 16 electrodes per well Machine Learning/classification paradigm Classification of spike train- derived activity pattern into discrete classes enables phenotype identification 300 sec MyCell Control Neurons at 7 div In vitro Alzheimer’s Disease modeling: functional biomarker for APP mutation in human neurons. * 0 50 100 150 200 250 300 350 400 450 7-8 div culture time Mean ± SEM ** * 0 1 2 3 4 5 6 7 7-8 div culture time Mean ± SEM * ** 0 2 4 6 8 10 12 14 16 18 7-8 div culture time Mean ± SEM *** * 0 0.1 0.2 0.3 0.4 0.5 7-8 div culture time Mean ± SEM [%] ** * 0 100 200 300 400 500 600 7-8 div culture time Mean ± SEM ** * 0 1 2 3 4 5 6 7 8 7-8 div culture time Mean ± SEM 0 0.2 0.4 0.6 0.8 1 1.2 7-8 div culture time Mean ± SEM * * 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 7-8 div culture time Mean ± SEM * ** 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 7-8 div culture time Mean ± SEM *** Spike rate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 7-8 div culture time Mean ± SEM [%] Spike contrast 0 0.05 0.1 0.15 0.2 0.25 0.3 7-8 div culture time Mean ± SEM [%] Burst rate 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 7-8 div culture time Mean ± SEM General activity Burst structure Oscillation Synchronicity Isogenic control Protective APP673T Causative APP673V Functional activity of control neurons (MyCell control, isogenic to mutants) and genetically engineered iPSC- derived neurons with mutations in the APP gene (amyloid precursor protein) leading to a causative genetic variant linked to Alzheimer’s Disease (APP673V) and a protective form (APP673T) not leading to the disease. Shown are 12 functional parameters indicating the phenotypic difference as a result of the APP673V mutation. Interestingly, these are not part of the general activity parameter category but seen in burst structure, oscillation and synchronicity. Isogenic control Causative APP673V mutation Spike train examples for spontaneous activity of APP673V mutant and isogenic control at 8 days in vitro show different patterns: e.g. Shown by longer bursts in APP673V. 300 sec 300 sec 300 sec Protective APP673V mutation Effect score ± SEM -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 control causative APP673V protective APP673T *** **** combination of all parameters * Position of a potential new AD drug? These results underline the need for multi-variate spike train analysis with focus on neuron-to-neuron communication. This approach provides a means to screen test compounds to rescue this disease- associated functional “biomarker” towards control conditions. “Effect Score” calculation: Projection of up to 204 parameters into a single parameter allows comparing multivariate data statistically and ranking of rescue efficacies based on the complete functional finger print. We calculate an optimized linear combination of selected features for an optimal separation of control effects from those of APP673V and APP673T. Control is set to “0”, APP673V is set to “1”. The calculated Z-factor (describing the effect size) is optimized to find the best discrimination between the two groups. Transcriptomic Analysis of APP673V, APP673T and control cells after MEA recording. MEA recording RNA quality control using Agilent 2100 Bioanalyzer Affymetrix whole human genome chip analysis Gene Ontology annotation Selection of regulated genes: Affimetrix analysis of MEA-recorded human neurons. Cells were harvested after MEA recording and RNA was isolated, quality controlled using Agilent 2100 Bioanalyzer. Whole genome Affymetrix GeneChip Human Transcriptome Arrays were used to investigate transcriptional differences between the isogenic lines. Gene Ontology analyses revealed more than 100 genes regulated with at least 1.5 fold above levels measured in isogenic control lines. The table shows a selection of genes which were expressed differently between mutants and control (at least two-fold). Multiple genes involved in synaptic plasticity, membrane organization, axonal transport were affected and thus, are directly involved in synaptic functional which is one explanation for the functional phenotypic difference observed in the MEA recordings. Moreover, genes involved in cellular signaling (MAPK, PKA, canonical Wnt), oxidative stress response are differently expressed. Genes involved in gene splicing, protein expression and nuclear transport were upregulated in causative APP673V mutant cells. RNA isolation mage fro th m r isher c m I m e e of .o Supported by: