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T-lymphocytes (T-cells) trafficking in the lymph nodes (LNs) is key for T-cells activation and their effector functions in adaptive immune responses. T-cells enter the LNs through high endothelial venules (HEVs) and interact with dendritic cells (DCs) for cognate antigens in the T-cell zone (TCZ). After scanning the TCZ for antigens, T-cells leave the LNs through efferent lymphatic vessel. CCR7 and its ligands, CCL19 and CCL21 are involved in the recruitment and compartmentalization of T-cells in LNs. However, their specific role(s) in mediating T-cells migration in LNs sub-regions remain unclear. In addition, the mechanism behind the passage of T-cells from the TCZ to the abluminal side of medullary sinuses (for their exit through medullary sinuses) is not well understood. Here, I hypothesize that different CCL19 and CCL21 fields in LNs sub-regions, orchestrate T-cells sub-regional migration in LNs.. In this study, I examined the CCL19 and CCL21 distribution profiles in mouse LNs sub-regions by immunofluoroscence staining and confocal microscopy. Using microfluidic devices that can flexibly configure well-defined single and co-existing chemical concentration gradients, I quantitatively analyzed the migration of activated human blood T-cells in LNs relevant CCL19 and CCL21 fields. The results suggested a novel CCL19 and CCL21 based combinatorial guiding mechanism for T-cells migration in different LNs sub-regions. In particular, this mechanism operates in the TCZ periphery region to guide T-cells migration away from the TCZ. Furthermore, the CCL19 and CCL21 fields mimicking the region beyond the TCZ toward the medulla result in disturbed chemotaxis, which prevents T-cells from being attracted back to the TCZ. Taken together, this microfluidics-based in vitro study shows the coordinated T-cells migration in different single and combined CCL19 and CCL21 fields, leading to interesting new insights into the guiding mechanisms for T-cells trafficking in LNs sub-regions.

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Objectives: Human Endogenous Retroviruses (HERVs) were originally derived from germ cell infection by exogenous retroviruses and comprise around eight per cent of the human genome. HERVs are highly expressed in the placenta, where HERV-W (syncytin 1) has been demonstrated to perform a fusogenic function. Due to their retroviral origin, placental syncytin 1 has been suggested to also be involved in modulating the maternal immune system. The placenta constantly sheds microvesicles (MV) into the maternal circulation, demonstrated to cause innate immune cell activation associated with normal pregnancy. In pre-eclampsia, there is both increased placental MV shedding and a heightened pro-inflammatory immune response. It was therefore hypothesised that HERVs shed via placental MV play a role in feto-maternal immune interactions and thus may be an important factor in the pathogenesis of preeclampsia (PE). More specifically, it was hypothesised that syncytin 1-positive MV activate monocytes through toll-like receptor 4 (TLR-4). The aim of this study was to determine if syncytin 1 is released from the placenta via MV and exerts an immunological effect. Methods: HERV mRNA and protein expression was measured in placenta and the BeWo choriocarcinoma cell line by qPCR, western blotting (WB) and immunostaining. Glycosylation of syncytin 1 protein was determined by PNGase F treatment followed by WB. MV shed by first trimester, term normal and PE placental explants as well as BeWo cells were isolated by ultracentrifugation. Morphology of these microvesicles was examined by electron microscopy. Syncytin 1 protein and RNA was detected in microvesicles by WB and PCR. Activation and priming of PBMCs to respond to lipopolysaccharide (LPS) by syncytin 1-positive MV and recombinant syncytin 1 was examined through cytokine production by ELISA and multiplex. Antagonism of TLR-4 by LPS-RS was used to determine involvement of the receptor. The role of syncytin 1 in MV activation was examined by siRNA knockdown. Results: HERVs are highly expressed in placental tissue. Syncytin 1 is a glycosylated protein and its expression is altered in PE. MV shed from the BeWo choriocarcinoma cell line and from first trimester and term placental explants, express HERV protein and RNA. Syncytin 1 positive MV and recombinant syncytin protein cause activation of PBMCs. Greatest activation is stimulated by PE MV. Normal MV exhibit a neutral or suppressive effect on subsequent LPS challenge to PBMCs. PE MV exacerbate the response to LPS. Antagonism of TLR-4 on PBMCs and knockdown of syncytin 1 content in MV reduces activation by placental MV.Conclusions: The findings of this thesis suggest that syncytin 1 protein expressed by the placenta is shed into the maternal circulation via MV, and can activate immune cells through TLR-4. Syncytin 1-positive microvesicles may play a role in endotoxin tolerance of innate immune cells in pregnancy. The increased activation by PE MV implies that in addition to the increased microvesicle load in this pathology, a factor intrinsic to PE MV is responsible for increased inflammation. These studies implicate microvesicle-bound syncytin 1 in the regulation of immunotolerance during pregnancy.

Immune Cell Potentiating Factor (ICPF) represents a class of biological response modifiers initially only found within active caprine serum fractions. Controlled studies have since demonstrated active ICPF derived from several non-caprine mammalian sources; including equine and human. ICPF is able to increase survivability in murine gram negative induced sepsis (60%) as well as secondary infection and subsequent sepsis in canines infected within canine parvo virus 2 (36%) despite showing no innate antiviral properties. ICPF is able to initiate systemic proteomic changes within several organ systems; including serum, liver, brain, lung, and spleen. ICPF initiated an early acute phase response, specifically through the increased expression of serum amyloid A, with systemic serum levels increasing from 1.5 μg/mL to 403.0 μg/mL within 24 hours and increased to 3,400 μg/mL within 48 hours following ICPF administration. Evaluation of cytokine expression following ICPF treatment revealed the up-regulation of IL-6, INF-γ, and the chemokine CXCL1\KC in vivo as well as the expression of IL-6 and IFN-γ in vitro within 3 hours of treatment. Development of an in vitro bioassay through the expression of IL-6 and IFN-γ within whole blood and peripheral blood mononuclear cells will allow for further elucidation and testing of ICPF outside of an animal host. The early expression of proinflammatory cytokines and chemokines, an acute phase response including serum amyloid A, and ICPF’s inability to alleviate mortality in a lipopolysaccharide animal mortality model strongly indicates an active role for ICPF as an immune regulatory peptide capable of promoting an early inflammatory response to Salmonella enterica serovar Typhimurium thereby reducing the risk and mortality associated with sepsis.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer death in the United States. Aldo-keto reductase 1 B10 (AKR1B10) is highly expressed in colon and small intestine of normal humans, but its expression is lost or markedly down-regulated in tissues of patients with ulcerative colitis (UC) and CRC. AKR1B10 is a monomeric cytosolic enzyme with strong enzymatic activity to α, β-unsaturated carbonyl compounds, protecting cells from carbonyl lesions; AKR1B10 also mediates de novo synthesis of long chain fatty acids and membrane lipids, such as phosphatidylinositol 4,5-bisphosphate (PIP2). To study the etiopathogenic role of AKR1B10 in UC and CRC, our lab generated AKR1B8 deficient (AKR1B8 -/-) mice. AKR1 B8 is the orthologue in mice of human AKR1B10,

京都大学 / 新制・課程博士 / 博士(医学) / 甲第25004号 / 医博第5038号 / 新制||医||1070(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 長船 健二, 教授 生田 宏一, 教授 上野 英樹 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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The immune defense is geared to protect against a tremendous array of invaders. The ultimate goal of the immune system is to induce effective and balanced inflammatory responses that enable the efficient elimination of possible threats while avoiding both immunodeficiency and autoimmunity. The skewness towards inflammatory responses causing excessive collateral damage could lead to diverse autoimmune conditions. These conditions could be organ-specific or result from systemic immune dysregulations called systemic autoimmunity. The multifaceted nature and the intricate clinical heterogeneity of systemic autoimmune conditions indicate a strong influence of environmental factors on their immunopathogenesis, where environmental factors could either hinder or contribute to autoimmune development. We focused our research on deciphering the complex effects of environmental factors on the immunopathogenesis of systemic immune dysregulation, taking systemic lupus erythematosus (SLE or Lupus) as a model of systemic autoimmunity. SLE is one of the most mysterious autoimmune disorders with no known cure. In SLE, breaching of tolerance to self-antigens and the subsequent persistent inflammation and collateral tissue damage in multiple organs lead to very diverse clinical manifestations. These manifestations are a result from the interplay between multiple genetic susceptibilities and diverse environmental factors. To date, management plans for SLE are based on non-selective immunosuppressants that could impose significant side effects including increased risks of infection and infection-related mortalities. In parallel, environmental factors and the quality of life could significantly impact SLE management strategies. Therefore, delineating the immunomodulatory capacities of environmental factors would likely unravel more effective management strategies for SLE patients. The current research aims to investigate the central hypothesis that dietary and hygienic components modulate the immune dysregulations of SLE in a tissue- and disease stage-specific manner. We have focused on uncovering the complex effects of Vitamin A (VA) as an essential micronutrient with very diverse immunomodulatory capacities, and quaternary ammonium compound (QAC)-based disinfectants as ubiquitously used disinfectants that have been linked to immunotoxicity, on the immunopathogenesis of SLE. Due to the strong female bias of SLE where women especially of childbearing age are more prone to lupus, we have focused our research on delineating how these diverse factors shape the immunopathogenesis of SLE in female mice only. The first project dissected the immunomodulatory effects of VA, a potent immunomodulatory dietary component. Notably, VA exerts its function through a predominant metabolite known as all-trans-retinoic acid (tRA) that, as we have previously shown, has paradoxical and tissue-specific implications on lupus inflammation. Here, we utilized a pristane-induced model of lupus to investigate the disease stage-dependent effects of tRA. Oral supplementation of tRA was given either before pristane induction of lupus from weaning (3 weeks) to 3 months of age or after pristane induction of lupus from 3 to 9 months of age. We found that tRA treatment mediated disease stage-dependent effects and differentially affected the lupus-associated kidney inflammation (lupus nephritis) when given at the initiation vs. continuation phase of lupus. Unlike tRA treatment during active disease, pre-pristane treatment with tRA aggravated glomerulonephritis through potentiating leukocyte activation and trafficking to the kidney and augmenting renal pro-fibrotic signals. Post-pristane tRA treatment, on the other hand, exerted immunosuppressive functions of decreasing circulatory and renal deposition of autoantibodies as well as suppressing the renal expression of proinflammatory cytokines and chemokines. Interestingly, both pre- and post-pristane treatments with tRA reversed the pristane-induced leaky gut and similarly modulated the gut microbiota, suggesting a gut microbiota-independent mechanism by which tRA affects the initiation vs. continuation phase of lupus. As tRA could be protective against lupus nephritis especially during the active disease stage, and previous reports had shown hypovitaminosis A (reduced serum retinol levels) proceeding SLE, we expanded our investigation to decipher whether VA deficiency (VAD) was a contributing factor for severe SLE and to delineate how VAD affected the initiation and/or the progression of lupus nephritis in genetically-prone conditions. For that purpose, we utilized the classical murine lupus-prone model, MRL/lpr, and initiated VAD either during the gestation or after weaning to reveal potential time-dependent effects. VAD exacerbated lupus nephritis by provoking severe neutrophilic tubulointerstitial nephritis, and accelerated renal failure. This was concomitant with significantly higher mortality in all VAD mice. Mechanistically, VAD enhanced early activation of plasma cells and augmented their autoantibodies production. In addition, VAD led to an enhanced expansion of pathogenic T lymphocytes. In parallel, VAD increased renal infiltration of conventional and plasmacytoid dendritic cells. Our findings establish VAD as a driving factor for lupus nephritis progression in genetically predisposed conditions. These findings emphasize the importance of monitoring VA levels in SLE patients and urge for VA supplementations for patients at higher risk for hypovitaminosis A, especially during the maternal-neonatal interface. Additionally, this project warrants further investigations to delineate the molecular targets through which VA modulates cellular functions as well as immunopathogenesis of lupus nephritis. The information obtained from these studies may also benefit women with other autoimmune conditions and will pave the way for VA supplementations to be tested in clinical trials. The second project investigated the effects of ambient exposure to QAC-based disinfectants on the progression of murine SLE in genetically prone mice. We compared the disease progression in MRL/Lpr mice that have been exposed to QACs vs. those kept under a complete QAC-free condition. QAC-based disinfectants CP-64 or Labsan 256 were used under QAC-exposed conditions, while ethanol was used in the QAC-free environment. We found that compared to QAC-free mice, ambient exposure of lupus-prone mice to QACs led to smaller spleens with no change in circulating autoantibodies or the severity of glomerulonephritis. This suggests that QACs may have immunosuppressive effects on lupus. Using a microfluidic device, we showed that ambient exposure to QACs reduced directional migration of bone marrow-derived neutrophils toward an inflammatory chemoattractant ex vivo. Consistent with this, we found decreased infiltration of neutrophils into the spleen. While bone marrow-derived neutrophils appeared to exhibit a pro-inflammatory profile, upregulated expression of PD-L1 was observed on neutrophils that infiltrated the spleen, which in turn interacted with PD-1 on T cells and modulated their fate. Specifically, QAC exposure hindered activation of splenic T cells and increased apoptosis of effector T-cell populations. Collectively, these results suggest that ambient QAC exposure decreases lupus-associated splenomegaly likely through neutrophil-mediated toning of T-cell activation and/or apoptosis. However, our findings also indicate that even ambient exposure could alter immune cell phenotypes, functions, and their fate. Further investigations on how QACs affect immunity under steady-state conditions are warranted. Collectively, the findings of this doctoral research suggest temporal and spatial effects of diet and hygiene on systemic autoimmunity and emphasize the strong influence of environmental factors toning cellular immune responses and subsequently shaping autoimmune outcomes. Our findings could pave the way for more personalized healthcare plans for autoimmune patients that take into consideration tissue involvement, disease stages, and the patient's lifestyle. / Doctor of Philosophy / The immune system is efficiently toned to discriminate between friends and foes. It effectively protects against a wide array of pathogens while at the same time avoiding attacking self-tissues. The inability of immune defenses to achieve this optimal discrimination could lead to the breakdown of tolerance to self in a wide range of autoimmune conditions. Diverse genetic susceptibilities are implicated in the development of autoimmunity. In parallel, during the recent decades, the tremendous increase in the prevalence of autoimmune conditions coincides with evolving dietary and hygiene styles in Westernized societies. This suggests a strong influence of environmental factors such as dietary and hygienic components on the way that the immune system works. Therefore, the current research investigates whether diet and hygiene modulate the immune dysregulations of lupus disease as a model for systemic autoimmunity; and if so, whether such effects are tissue- and/or disease stage-specific. We utilized different mouse models to delineate the mechanisms by which essential nutrients such as vitamin A (VA) and widely used disinfectant compounds known as quaternary ammonium disinfectants (QACs) modulate the systemic autoimmunity in lupus disease. We found that these modulators influence various aspects of the cellular immune responses including (1) leukocyte activation and subsequent expansion of pathogenic (disease contributing) lymphocytes, production of antibodies directed against self-tissue molecules (i.e., autoantibodies), and production of inflammatory mediators (i.e., cytokines and chemokines); (2) cell trafficking and their infiltration into the tissues; (3) signal transduction pathways that modulate cell fate (e.g., PD-1: PD-L1 signaling). Importantly, environmental modulation of autoimmunity during different stages of autoimmune development could significantly impact the disease outcome. VA treatment, for example, differentially modulates the progression of kidney inflammation when given during the initiation vs. progressive disease stages. Similarly, VA deficiency has the most prominent effects on worsening kidney inflammation under genetically prone conditions when the deficiency is initiated early and at the prenatal stage. In parallel, the effects of environmental factors are also tissue-specific. For example, ambient exposure to QAC-based disinfectants exerted immunosuppressive effects on lupus-associated inflammation of lymphoid tissues with no change in circulating autoantibodies or the severity of kidney inflammation. Collectively, the findings of this doctoral research delineated the cellular mechanisms through which environmental factors could shape autoimmune responses. Further studies will dig into the underlying molecular pathways. Ultimately, our research emphasizes the strong influence of exogenous factors on immunity and will pave the way for more effective healthcare management plans and benefit vulnerable populations affected by autoimmune conditions such as lupus.

Technologies such as micro-expression arrays and high-throughput sequenc- ing assays have accelerated research of genetic transcription in biological cells. Furthermore, many links between the gene expression levels and the pheno- typic characteristics of cells have been discovered. Our current understanding of transcriptomics as an intermediate regulatory layer between genomics and proteomics raises hope that we will soon be able to decipher many more cel- lular mechanisms through the exploration of gene transcription. However, although large amounts of expression data are measured, only lim- ited information can be extracted. One general problem is the large set of considered genomic features. Expression levels are often analyzed individually because of limited computational resources and unknown statistical dependen- cies among the features. This leads to multiple testing issues or can lead to overfitting models, commonly referred to as the “curse of dimensionality.” Another problem can arise from ignorance of measurement uncertainty. In particular, approaches that consider statistical significance can suffer from underestimating uncertainty for weakly expressed genes and consequently re- quire subjective manual measures to produce consistent results (e.g., domain- specific gene filters). In this thesis, we lay out a theoretical foundation for a Bayesian interpretation of gene expression data based on subtle assumptions. Expression measure- ments are related to latent information (e.g., the transcriptome composition), which we formulate as a probability distribution that represents the uncer- tainty over the composition of the original sample. Instead of analyzing univariate gene expression levels, we use the multivari- ate transcriptome composition space. To realize computational feasibility, we develop a scalable dimensional reduction that aims to produce the best approximation that can be used with the computational resources available. To enable the deconvolution of gene expression, we describe subtissue specific probability distributions of expression profiles. We demonstrate the suitabil- ity of our approach with two deconvolution applications: first, we infer the composition of immune cells, and second we reconstruct tumor-specific ex- pression patterns from bulk-RNA-seq data of prostate tumor tissue samples.:1 Introduction 1 1.1 State of the Art and Motivation 2 1.2 Scope of this Thesis 5 2 Notation and Abbreviations 7 2.1 Notations 7 2.2 Abbreviations 9 3 Methods 10 3.1 The Convolution Assumption 10 3.2 Principal Component Analysis 11 3.3 Expression Patterns 11 3.4 Bayes’ Theorem 12 3.5 Inference Algorithms 13 3.5.1 Inference Through Sampling 13 3.5.2 Variationa lInference 14 4 Prior and Conditional Probabilities 16 4.1 Mixture Coefficients 16 4.2 Distribution of Tumor Cell Content 18 4.2.1 Optimal Tumor Cell Content Drawing 20 4.3 Transcriptome Composition Distribution 21 4.3.1 Sequencing Read Distribution 21 4.3.1.1 Empirical Plausibility Investigation 25 4.3.2 Dirichletand Normality 29 4.3.3 Theta◦logTransformation 29 4.3.4 Variance Stabilization 32 4.4 Cell and Tissue-Type-Specific Expression Pattern Distributions 32 4.4.1 Method of Moments and Factor Analysis 33 4.4.1.1 Tumor Free Cells 33 4.4.1.2 Tumor Cells 34 4.4.2 Characteristic Function 34 4.4.3 Gaussian Mixture Model 37 4.5 Prior Covariance Matrix Distribution 37 4.6 Bayesian Survival Analysis 38 4.7 Demarcation from Existing Methods 40 4.7.1 Negative Binomial Distribution 40 4.7.2 Steady State Assumption 41 4.7.3 Partial Correlation 41 4.7.4 Interaction Networks 42 5 Feasibility via Dimensional Reduction 43 5.1 DR for Deconvolution of Expression Patterns 44 5.1.1 Systematically Differential Expression 45 5.1.2 Internal Distortion 46 5.1.3 Choosinga DR 46 5.1.4 Testing the DR 47 5.2 Transformed Density Functions 49 5.3 Probability Distribution of Mixtures in DR Space 50 5.3.1 Likelihood Gradient 51 5.3.2 The Theorem 52 5.3.3 Implementation 52 5.4 DR for Inference of Cell Composition 53 5.4.1 Problem Formalization 53 5.4.2 Naive PCA 54 5.4.3 Whitening 55 5.4.3.1 Covariance Inflation 56 5.4.4 DR Through Optimization 56 5.4.4.1 Starting Point 57 5.4.4.2 The Optimization Process 58 5.4.5 Results 59 5.5 Interpretation of DR 61 5.6 Comparison to Other DRs 62 5.6.1 Weighted Correlation Network Analysis 62 5.6.2 t-Distributed Stochastic Neighbor Embedding 65 5.6.3 Diffusion Map 66 5.6.4 Non-negativeMatrix Factorization 66 5.7 Conclusion 67 6 Data for Example Application 68 6.1 Immune Cell Data 68 6.1.1 Provided List of Publicly Available Data 68 6.1.2 Obtaining the Publicly Available RNA-seq Data 69 6.1.3 Obtaining the Publicly Available Expression Microarray Data 71 6.1.4 Data Sanitization 71 6.1.4.1 A Tagging Tool 72 6.1.4.2 Tagging Results 73 6.1.4.3 Automatic Sanitization 74 6.1.5 Data Unification 75 6.1.5.1 Feature Mapping 76 6.1.5.2 Feature Selection 76 6.2 Examples of Mixtures with Gold Standard 79 6.2.1 Expression Microarray Data 81 6.2.2 Normalized Expression 81 6.2.3 Composition of the Gold Standard 82 6.3 Tumor Expression Data 82 6.3.1 Tumor Content 82 6.4 Benchmark Reference Study 83 6.4.1 Methodology 83 6.4.2 Reproduction 84 6.4.3 Reference Hazard Model 85 7 Bayesian Models in Example Applications 87 7.1 Inference of Cell Composition 87 7.1.1 The Expression Pattern Distributions (EPDs) 88 7.1.2 The Complete Model 89 7.1.3 Start Values 89 7.1.4 Resource Limits 90 7.2 Deconvolution of Expression Patterns 91 7.2.1 The Distribution of Expression Pattern Distribution 91 7.2.2 The Complete Model 92 7.2.3 SingleSampleDeconvolution 93 7.2.4 A Simplification 94 7.2.5 Start Values 94 8 Results of Example Applications 96 8.1 Inference of Cell Composition 96 8.1.1 Single Composition Output 96 8.1.2 ELBO Convergence in Variational Inference 97 8.1.3 Difficulty-Divergence 97 8.1.3.1 Implementing an Alternative Stick-Breaking 98 8.1.3.2 Using MoreGeneral Inference Methods 99 8.1.3.3 UsingBetterData 100 8.1.3.4 Restriction of Variance of Cell-Type-Specific EPDs 100 8.1.3.5 Doing Fewer Iterations 100 8.1.4 Difficulty-Bias 101 8.1.5 Comparison to Gold Standard 101 8.1.6 Comparison to Competitors 101 8.1.6.1 Submission-Aginome-XMU 105 8.1.6.2 Submission-Biogem 105 8.1.6.3 Submission-DA505 105 8.1.6.4 Submission-AboensisIV 105 8.1.6.5 Submission-mittenTDC19 106 8.1.6.6 Submission-CancerDecon 106 8.1.6.7 Submission-CCB 106 8.1.6.8 Submission-D3Team 106 8.1.6.9 Submission-ICTD 106 8.1.6.10 Submission-Patrick 107 8.1.6.11 Conclusion for the Competitor Review 107 8.1.7 Implementation 107 8.1.8 Conclusion 108 8.2 Deconvolution of Expression Patterns 108 8.2.1 Difficulty-Multimodality 109 8.2.1.1 Order of Kernels 109 8.2.1.2 Posterior EPD Complexity 110 8.2.1.3 Tumor Cell Content Estimate 110 8.2.2 Difficulty-Time 110 8.2.3 The Inference Process 111 8.2.3.1 ELBO Convergence in Variational Inference 111 8.2.4 Posterior of Tumor Cell Content 112 8.2.5 Posterior of Tissue Specific Expression 112 8.2.6 PosteriorHazardModel 113 8.2.7 Gene Marker Study with Deconvoluted Tumor Expression 115 8.2.8 Hazard Model Comparison Overview 116 8.2.9 Implementation 116 9 Discussion 117 9.1 Limitations 117 9.1.1 Simplifying Assumptions 117 9.1.2 Computation Resources 118 9.1.3 Limited Data and Suboptimal Format 118 9.1.4 ItIsJustConsistency 119 9.1.5 ADVI Uncertainty Estimation 119 9.2 Outlook 119 9.3 Conclusion 121 A Appendix 123 A.1 Optimalα 123 A.2 Digamma Function and Logarithm 123 A.3 Common Normalization 124 A.3.1 CPMNormalization 124 A.3.2 TPMNormalization 124 A.3.3 VSTNormalization 125 A.3.4 PCA After Different Normalizations 125 A.4 Mixture Prior Per Tissue Source 125 A.5 Data 125 A.6 Cell Type Characterization without Whitening 133 B Proofs 137 Bibliography 140