Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Characterisation of Aeromonas strains and species by pulsed field gel electrophoresis and principal components analysis

Aeromonas genomes were investigated by restriction digesting chromosomal DNA with the endonuclease XbaI, separation of restriction fragments by pulsed field gel electrophoresis (PFGE) and principal components analysis (PCA) of resulting separation patterns. A. salmonicida salmonicida were unique amongst the isolates investigated. Separation profiles of these isolates were similar and all characterised by a distinct absence of bands in the 250kb region. Principal components analysis represented these strains as a clearly defined homogeneous group separated by insignificant Euclidian distances. However, A. salmonicida achromogenes isolates in common with those of A. hydrophila and A. sobria were shown by principal components analysis to be more heterogeneous in nature. Fragments from these isolates were more uniform in size distribution but as demonstrated by the Euclidian distances attained through PCA potentially characteristic of each strain. Furthermore passaging of Aeromonas isolates through an appropriate host did not greatly modify fragment separation profiles, indicative of the genomic stability of test aeromonads and the potential of restriction digesting/PFGE/PCA in Aeromonas typing.

Analysis of plasmid profiles of Aeromonas salmonicida isolates by pulsed field gel electrophoresis

Plasmid constitutions of Aeromonas salmonicida isolates were characterised by flat-bed and pulsed field gel electrophoresis. Resolution of plasmids by pulsed field gel electrophoresis was greater and more consistent than that achieved by flat-bed gel electrophoresis. The number of plasmids separated by pulsed field gel electrophoresis varied between A. salmonicida isolates, with five being the most common number present in the isolates used in this study. Plasmid profiles were diverse and the reproducibility of the distances migrated facilitated the use of principal components analysis for the characterisation of the isolates. Isolates were grouped according to the number of plasmids supported. Further principal components analysis of groups of isolates supporting five and seven plasmids showed a spatial separation of plasmids based upon distance migrated. Principal components analysis of plasmid profiles and antimicrobial minimum inhibitory concentrations could not be correlated suggesting that resistance to antimicrobial agents is not associated with either one plasmid or a particular plasmid constitution.

Single-cell ELISA and flow cytometry as methods for highlighting potential neuronal and astrocytic toxicant specificity

The timeline imposed by recent worldwide chemical legislation is not amenable to conventional in vivo toxicity testing, requiring the development of rapid, economical in vitro screening strategies which have acceptable predictive capacities. When acquiring regulatory neurotoxicity data, distinction on whether a toxic agent affects neurons and/or astrocytes is essential. This study evaluated neurofilament (NF) and glial fibrillary acidic protein (GFAP) directed single-cell (S-C) ELISA and flow cytometry as methods for distinguishing cell-specific cytoskeletal responses, using the established human NT2 neuronal/astrocytic (NT2.N/A) co-culture model and a range of neurotoxic (acrylamide, atropine, caffeine, chloroquine, nicotine) and non-neurotoxic (chloramphenicol, rifampicin, verapamil) test chemicals. NF and GFAP directed flow cytometry was able to identify several of the test chemicals as being specifically neurotoxic (chloroquine, nicotine) or astrocytoxic (atropine, chloramphenicol) via quantification of cell death in the NT2.N/A model at cytotoxic concentrations using the resazurin cytotoxicity assay. Those neurotoxicants with low associated cytotoxicity are the most significant in terms of potential hazard to the human nervous system. The NF and GFAP directed S-C ELISA data predominantly demonstrated the known neurotoxicants only to affect the neuronal and/or astrocytic cytoskeleton in the NT2.N/A cell model at concentrations below those affecting cell viability. This report concluded that NF and GFAP directed S-C ELISA and flow cytometric methods may prove to be valuable additions to an in vitro screening strategy for differentiating cytotoxicity from specific neuronal and/or astrocytic toxicity. Further work using the NT2.N/A model and a broader array of toxicants is appropriate in order to confirm the applicability of these methods.

Acanthamoeba polyphaga trophozoite binding of representative fungal single cell forms

Acanthamoeba polyphaga trophozoites bind yeast cells of Candida albicans isolates within a few hours, leaving few cells in suspension or still attached to trophozoite surfaces. The nature of yeast cell recognition, mediated by an acanthamoebal trophozoite mannose binding protein is confirmed by experiments utilizing concentration dependent mannose hapten blocking. Similarly, acapsulate cells of Cryptococcus neoformans are also bound within a relatively short timescale. However, even after protracted incubation many capsulate cells of Cryptococcus remain in suspension, suggesting that the capsulate cell form of this species is not predated by acanthamoebal trophozoites. Further aspects of the association of Acanthamoeba and fungi are apparent when studying their interaction with conidia of the biocontrol agent Coniothyrium minitans. Conidia which readily bind with increasing maturity of up to 42 days, were little endocytosed and even released. Cell and conidial surface mannose as determined by FITC-lectin binding, flow cytometry with associated ligand binding analysis and hapten blocking studies demonstrates the following phenomena. Candida isolates and acapsulate Cryptococcus expose most mannose, while capsulate Cryptococcus cells exhibit least exposure commensurate with yeast cellular binding or lack of trophozoites. Conidia of Coniothyrium, albeit in a localized fashion, also manifest surface mannose exposure but as shown by Bmax values, in decreasing amounts with increasing maturity. Contrastingly such conidia experience greater trophozoite binding with maturation, thereby questioning the primacy of a trophozoite mannose-binding-protein recognition model.

Single-cell analysis for bioprocessing

Cell population heterogeneity has attracted great interest for understanding the individual cellular performances in their response to external stimuli and in the production of targeted products. Physical characterization of single cells and analysis of dynamic gene expression, synthesized proteins, and cellular metabolites from one single cell are reviewed. Advanced techniques have been developed to achieve high-throughput and ultrahigh resolution or sensitivity. Single cell capture methods are discussed as well. How to make use of cellular heterogeneities for maximizing cellular productivity is still in the infant stage, and control strategies will be formulated after the causes for heterogeneity have been elucidated.

One-dimensional and two-dimensional polyacrylamide gel electrophoresis: a tool for protein characterisation in aquatic samples

Dissolved organic nitrogen (DON) represents the least understood part of the nitrogen cycle. Due to recent methodological developments, proteins now represent a potentially characterisable fraction of DON at the macromolecular level. We have applied polyacrylamide gel electrophoresis to characterise proteins in samples from a range of aquatic environments in the Everglades National Park, Florida, USA. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) showed that each sample has a complex and characteristic protein distribution. Some proteins appeared to be common to more than one site, and these might derive from dominant higher plant vegetation. Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) provided better resolution; however, strong background hindered interpretation. Our results suggest that the two techniques can be used in parallel as a tool for protein characterisation: SDS-PAGE to provide a sample-specific fingerprint and 2D-PAGE to focus on the characterisation of individual protein molecules.

Single-cell functional analysis of parathyroid adenomas reveals distinct classes of calcium sensing behaviour in primary hyperparathyroidism.

Primary hyperparathyroidism (PHPT) is a common endocrine neoplastic disorder caused by a failure of calcium sensing secondary to tumour development in one or more of the parathyroid glands. Parathyroid adenomas are comprised of distinct cellular subpopulations of variable clonal status that exhibit differing degrees of calcium responsiveness. To gain a clearer understanding of the relationship among cellular identity, tumour composition and clinical biochemistry in PHPT, we developed a novel single cell platform for quantitative evaluation of calcium sensing behaviour in freshly resected human parathyroid tumour cells. Live-cell intracellular calcium flux was visualized through Fluo-4-AM epifluorescence, followed by in situ immunofluorescence detection of the calcium sensing receptor (CASR), a central component in the extracellular calcium signalling pathway. The reactivity of individual parathyroid tumour cells to extracellular calcium stimulus was highly variable, with discrete kinetic response patterns observed both between and among parathyroid tumour samples. CASR abundance was not an obligate determinant of calcium responsiveness. Calcium EC50 values from a series of parathyroid adenomas revealed that the tumours segregated into two distinct categories. One group manifested a mean EC50 of 2.40 mM (95% CI: 2.37-2.41), closely aligned to the established normal range. The second group was less responsive to calcium stimulus, with a mean EC50 of 3.61 mM (95% CI: 3.45-3.95). This binary distribution indicates the existence of a previously unappreciated biochemical sub-classification of PHPT tumours, possibly reflecting distinct etiological mechanisms. Recognition of quantitative differences in calcium sensing could have important implications for the clinical management of PHPT.

Single-Cell Transcriptome Analysis of Olfactory Sensory Neurons

Olfactory sensory neurons (OSNs), which detect a myriad of odorants, are known to express one allele of one olfactory receptor (OR) gene (Olfr) from the largest gene family in the mammalian genome. The OSNs expressing the same OR project their axons to the main olfactory bulb where they converge to form glomeruli. This “One neuron-one receptor rule” makes the olfactory epithelium (OE), which consists of a vast number of OSNs expressing unique ORs, one of the most heterogeneous cell populations. However, the mechanism of how the single OR allele is chosen remains unclear along with the question of whether one OSN only expresses a single OR gene, a hypothesis that has not been rigorously verified while we performed the experiments. Moreover, failure of axonal targeting to single glomerulus was observed in MeCP2 deficient OSNs where delayed development was proposed as an explanation for the phenotype. How Mecp2 mutation caused this aberrant targeting is not entirely understood.

In this dissertation, we explored the transcriptomes of single and mature OSNs by single-cell RNA-Seq to reveal their heterogeneity and further studied the OR gene expression from these isolated OSNs. The singularity of sequenced OSNs was ensured by the observation of monoallelic expression of X-linked genes from the hybrid samples from crosses between mice of different strains where strain-specific polymorphisms could be used to track the allelic origins of SNP-containing reads. The clustering of expression profiles from triplicates that originated from the same cell assured that the transcriptomic identities of OSNs were maintained through the experimental process. The average gene expression profiles of sequenced OSNs correlated well to the conventional transcriptome data of FACS-sorted Omp-positive cells, and the top-ranked expression of OR was conceded in the single-OSN transcriptomes. While exploring cellular diversity, in addition to OR genes, we revealed nearly 200 differentially expressed genes among the sequenced OSNs in this study. Among the 36 sequenced OSNs, eight cells (22.2%) showed multiple OR gene expression and the presences of additional ORs were not restricted to the neighbor loci that shared the transcriptional effect of the primary OR expression, suggesting that the “One neuron-one receptor rule” might not be strictly true at the transcription level. All of the inferable ORs, including additional co-expressed ORs, were shown to be monoallelic. Our sequencing of 21 Mecp2308 mutant OSNs, of which 62% expressed more than one OR genes, and the expression levels of the additional ORs were significantly higher than those in the wild-type, suggested that MeCP2 plays a role in the regulation of singular OR gene expression. Dual label in situ hybridization along with the sequence data revealed that dorsal and ventral ORs were co-expressed in the same Mecp2 mutant OSN, further implying that MeCP2 might be involved in regulation of OR territories in the OE. Our results suggested a new role of MeCP2 in OR gene choice and ratified that this multiple-OR expression caused by Mecp2 mutation did not accompany delayed OSN development that has been observed in the previous studies on the Mecp2 mutants.

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.

Purification, biochemical characterization and localization at single cell resolution of Matrix Gla Protein (MGP) and Bone Gla Protein (BGP) in the teleost fish Argyrosomus regius

Tese de dout. em Química, Faculdade de Ciências do Mar e do Ambiente, Univ. do Algarve, 2002

Phenotypic and molecular investigation of circulating tumor cells (CTCs) isolated from breast cancer patients at single cell resolution

Despite the paramount advances in cancer research, breast cancer (BC) still ranks one of the leading causes of cancer-related death worldwide. Thanks to the screening campaign started in developed countries, BC is often diagnosed at early stages (non-metastatic BC, nmBC), but disease relapse occurrence even after decades and at distant sites is not an uncommon phenomenon. Conversely, metastatic BC (mBC) is considered an incurable disease. The major perpetrators of tumor spread to secondary organs are circulating tumor cells (CTCs), a rare population of cells detectable in the peripheral blood of oncologic patients. In this study, CTCs from patients diagnosed with luminal nmBC and mBC (hormone receptor positive, Human Epidermal Growth Factor Receptor 2 (HER2) negative) were characterized at both phenotypic and molecular levels. To better understand the molecular mechanisms underlying their biology and their metastatic potential, next-generation sequencing (NGS) analyses were performed at single-cell resolution to assess copy number aberrations (CNAs), single nucleotide variants (SNVs) and gene expression profiling. The findings of this study arise hints in CTC detection, and pave the way to new application in CTC research.