Identifying properties and concentrations of components from an observed mixture, known as deconvolution, is a fundamental problem in signal processing. It has diverse applications in fields ranging from hyperspectral imaging to noise cancellation in audio recordings. This paper focuses on in-silico deconvolution of signals associated with complex tissues into their constitutive cell-type-specific components and a quantitative characterization of the cell types. Deconvolving mixed tissues/cell types is useful in the removal of contaminants (e.g., surrounding cells) from tumor biopsies, as well as in monitoring changes in the cell population in response to treatment or infection. In these contexts, the observed signal from the mixture of cell types is assumed to be a convolution, using a linear instantaneous (LI) mixing process, of the expression levels of genes in constitutive cell types. The goal is to use known signals corresponding to individual cell types and a model of the mixing process to cast the deconvolution problem as a suitable optimization problem. In this paper, we present a survey and in-depth analysis of models, methods, and assumptions underlying deconvolution techniques. We investigate the choice of the different loss functions for evaluating estimation error, constraints on solutions, preprocessing and data filtering, feature selection, and regularization to enhance the quality of solutions and the impact of these choices on the performance of commonly used regression-based methods for deconvolution. We assess different combinations of these factors and use detailed statistical measures to evaluate their effectiveness. Some of these combinations have been proposed in the literature, whereas others represent novel algorithmic choices for deconvolution. We identify shortcomings of current methods and avenues for further investigation. For many of the identified shortcomings, such as normalization issues and data filtering, we provide new solutions. We summarize our findings in a prescriptive step-by-step process, which can be applied to a wide range of deconvolution problems.

The contribution of antigen‐driven B‐cell adaptive immune responses within the inflamed muscle of inflammatory myopathies (IMs) is largely unknown. In this study, we investigated the immunoglobulin VH gene repertoire, somatic hypermutation, clonal diversification, and selection of infiltrating B cells in muscle biopsies from IM patients (dermatomyositis and polymyositis), to determine whether B cells and/or plasma cells contribute to the associated pathologies of these diseases. The data reveal that Ig VH gene repertoires of muscle‐infiltrating B cells deviate from the normal VH gene repertoire in individual patients, and differ between different types of IMs. Analysis of somatic mutations revealed clonal diversification of muscle‐infiltrating B cells and evidence for a chronic B‐cell response within the inflamed muscle. We conclude that muscle‐infiltrating B cells undergo selection, somatic hypermutation and clonal diversification in situ during antigen‐driven immune responses in patients with IMs, providing insight into the contribution of B cells to the pathological mechanisms of these disorders.

The immune response involves cells of various types, including B, T and Natural Killer (NK) lymphocytes expressing a large diversity of receptors which recognize foreign antigens and self-molecules. The various cell types interact through a complicated network of communication and regulation mechanisms. These interactions enable the immune system to perform the functions of danger recognition, decision, action, memory and learning. As a result, the dynamics of lymphocyte repertoires are highly complex and non-linear. The humoral (antibody-generating) immune response is one of the most complex responses, as it involves somatic hypermutation of the B cell receptor (BCR) genes and subsequent antigen-driven selection of the resulting mutants. This process has been and still is extensively studied using a variety of experimental methods - ranging from intravital imaging to studying the mutations in BCR genes - and has also been one of the most often modeled phenomena in the theoretical immunology community. The problem for modelers, however, is that until recently kinetic data on the humoral immune response were so limited that all models could fit those data. We have addressed this - and the challenge of following individual clones - by combining modeling with a novel immuno-informatical method of generation and quantification of lineage trees from B cell clones undergoing somatic hypermutation. We applied these new analyses to the study of humoral response changes in aging, chronic or autoimmune diseases and B cell malignancies. Finally, we used simulations to answer some theoretical questions regarding the evolution of BCR genes.

B cells have been found to play a critical role in the pathogenesis of several autoimmune (AI) diseases. A common feature amongst many AI diseases is the formation of ectopic germinal centers (GC) within the afflicted tissue or organ, in which activated B cells expand and undergo somatic hypermutation (SHM) and antigen-driven selection on their immunoglobulin variable region (IgV) genes. However, it is not yet clear whether these processes occurring in ectopic GCs are identical to those in normal GCs. The analysis of IgV mutations has aided in revealing many aspects concerning B cell expansion, mutation and selection in GC reactions. We have applied several mutation analysis methods, based on lineage tree construction, to a large set of data, containing IgV productive and non-productive heavy and light chain sequences from several different tissues, to examine three of the most profoundly studied AI diseases - Rheumatoid Arthritis (RA), Multiple Sclerosis (MS) and Sjögren's Syndrome (SS). We have found that RA and MS sequences exhibited normal mutation spectra and targeting motifs, but a stricter selection compared to normal controls, which was more apparent in RA. SS sequence analysis results deviated from normal controls in both mutation spectra and indications of selection, also showing differences between light and heavy chain IgV and between different tissues. The differences revealed between AI diseases and normal control mutation patterns may result from the different microenvironmental influences to which ectopic GCs are exposed, relative to those in normal secondary lymphoid tissues.

Follicular lymphoma (FL), diffuse large B cell lymphoma (DLBCL) and primary central nervous system lymphoma are B cell malignancies. FL and DLBCL have a germinal center origin. We have applied mutational analyses and a novel algorithm for quantifying shape properties of mutational lineage trees to investigate the nature of the diversification, somatic hypermutation and selection processes that affect B cell clones in these malignancies and reveal whether they differ from normal responses. Lineage tree analysis demonstrated higher diversification and mutations per cell in the lymphoma clones. This was caused solely by the longer diversification times of the malignant clones, as their recent diversification processes were similar to those of normal responses, implying similar mutation frequencies. Since previous analyses of antigen-driven selection were shown to yield false positives, we performed a corrected analysis of replacement and silent mutation patterns, which revealed selection against replacement mutations in the framework regions, responsible for the structural integrity of the B cell receptor, but not for positive selection for replacements in the complementary determining regions. Most replacements, however, were neutral or conservative, suggesting that if at all selection operates in these malignancies it is for structural B cell receptor integrity but not for antigen binding.

Young patients with myasthenia gravis (MG) frequently have ectopic GC in their thymus. We investigated these ectopic GC by microdissection of GC B cells and analysis of their Ig gene characteristics, in comparison to normal GC. CDR3 length distribution, a measure of clonal variability, and Ig gene family usage were similar in MG and normal tonsil samples. Lineage tree analysis demonstrated similar diversification and mutations per cell compared with normal control trees. Mutations were observed in the framework regions, responsible for the structural integrity of the BCR; however, these mutations were mostly conservative or neutral, confirming that a functional BCR is conserved in MG. In the CDR, responsible for Ag binding, selection against replacement mutations was revealed. This may indicate that the MG clones analyzed are already highly Ag-specific, and therefore potential affinity-reducing replacement mutations in the CDR3 are not propagated, due to Ag-driven selection. Somatic hypermutation (SHM) targeting motifs and aa substitution preferences in MG were similar to those of normal controls. Overall, these results suggest that B cells in the ectopic GC in MG appear to undergo normal diversification and selection, in spite of the chronic nature and different environment of the response.

During the immune response, the generation of memory B lymphocytes in germinal centers involves affinity maturation of the cells' antigen receptors, based on somatic hypermutation of receptor genes and antigen-driven selection of the resulting mutants. Affinity maturation is vital for immune protection, and is the basis of humoral immune learning and memory. Lineage trees of somatically hypermutated immunoglobulin genes often serve to qualitatively illustrate claims concerning the dynamics of affinity maturation in germinal centers. Here, we derive the quantitative relationships between parameters characterizing affinity maturation dynamics (proliferation, differentiation and mutation rates, initial affinity of the Ig to the antigen, and selection thresholds) and the mathematical properties of lineage trees, using a computer simulation which combines mathematical models for all mature B cell populations, stochastic models of hypermutation and selection, lineage tree generation and measurement of graphical tree characteristics. We identified seven key lineage tree properties, and found correlations of these with initial clone affinity and with the selection threshold. These two parameters were found to be the main factors affecting lineage tree shapes in both primary and secondary response trees. The results also confirm that recycling from centrocytes back to centroblasts is highly likely.

Lineage trees describe the microevolution of cells within an organism. They have been useful in the study of B cell affinity maturation, which is based on somatic hypermutation of immunoglobulin genes in germinal centers and selection of the resulting mutants. Our aim was to create and implement an algorithm that can generate lineage trees from immunoglobulin variable region gene sequences. The IgTree© program implements the algorithm we developed, and generates lineage trees. Original sequences found in experiments are assigned to either leaves or internal nodes of the tree. Each tree node represents a single mutation separating the sequences. The mutations that separate the sequences from each other can be point mutations, deletions or insertions. The program can deal with gaps and find potential reversion mutations. The program also enumerates mutation frequencies and sequence motifs around each mutation, on a per-tree basis. The algorithm has proven useful in several studies of immunoglobulin variable region gene mutations.

In studies of inflammatory bowel diseases (IBD), research has so far focused mainly on the role of T cells. Despite evidence suggesting that B cells and the production of autoantibodies may play a significant role in IBD pathogenesis, the role of B cells in gut inflammation has not yet been thoroughly investigated. In the present study we used the new approach of lineage tree analysis for studying immunoglobulin variable region gene diversification in B cells found in the inflamed intestinal tissue of two ulcerative colitis patients as well as B cells from mucosa-associated lymph nodes (LN) in the same patients. Healthy intestinal tissue of three patients with carcinoma of the colon was used as normal control. Lineage tree shapes revealed active immune clonal diversification processes occurring in ulcerative colitis patients, which were quantitatively similar to those in healthy controls. B cells from intestinal tissues and the associated LN are shown here to be clonally related, thus supplying the first direct evidence supporting B-cell trafficking between gut and associated LN in IBD and control tissues.

Light chain amyloidosis (AL) is a bone marrow (BM) plasma cell neoplasia with systemic deposition of Ig light chain amyloid fibrils. Here, we report the identification of clonal CD19 B cells in the BM and the use of a novel mathematical algorithm to generate B cell lineage trees of the clonal CD19 B cells and CD138 plasma cells from the BM of AL patients to delineate the relationship between these two clonal populations. The CD19+ clonal B cells in the BM of AL patients related to the clonal plasma cells represent a pre-plasma cell precursor population. The B cell lineage trees from AL patients also show significant differences in clonal diversification and antigenic selection compared to clones from normal, healthy controls. These data provide a robust example of the use of graphical quantification methods in delineating the role of neoplastic precursors in the pathogenesis of hematopoietic malignancies.

Immunoglobulin light chain amyloidosis (AL) is characterized by a limited clonal expansion of plasma cells and amyloid formation. Here, we report restriction in the diversity of VL gene usage with a dominance of clonally related B cells in the peripheral blood (PB) isotype-specific repertoire of AL patients. A rigorous quantification of lineage trees reveals presence of intraclonal variations in the PB clones compared to the bone marrow ( BM) clones, which suggests a common precursor that is still subject to somatic mutation. When compared to normal BM and PB B cells, AL clones showed significant but incomplete impairment of antigenic selection, which could not be detected by conventional R and S mutation analysis. Therefore, graphical analysis of B cell lineage trees and mathematical quantification of tree properties provide novel insights into the process of B cell clonal evolution in AL.

Autoimmune diseases show high diversity in the affected organs, clinical manifestations and disease dynamics. Yet they all share common features, such as the ectopic germinal centers found in many affected tissues. Lineage trees depict the diversification, via somatic hypermutation (SHM), of immunoglobulin variable-region (IGV) genes. We previously developed an algorithm for quantifying the graphical properties of IGV gene lineage trees, allowing evaluation of the dynamical interplay between SHM and antigen-driven selection in different lymphoid tissues, species, and disease situations. Here, we apply this method to ectopic GC B cell clones from patients with Myasthenia Gravis, Rheumatoid Arthritis, and Sjögren’s Syndrome, using data scaling to minimize the effects of the large variability due to methodological differences between groups. Autoimmune trees were found to be significantly larger relative to normal controls. In contrast, comparison of the measurements for tree branching indicated that similar selection pressure operates on autoimmune and normal control clones.