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Systematically inferring directional effects within cell states from single-cell data
No full textA deeper understanding of the molecular wiring of cells in health—and where it goes awry in disease—can enable the development of more precise therapies that target not just consequences, but also the root causes of devastating ailments. Over the past decade, advances in human genetics and technologies to assay biology at single-cell resolution have introduced high-resolution strategies to define causal mechanisms of disease. Genome-wide association studies have highlighted genetic variants that are associated with hundreds of complex traits; in parallel, single-cell RNA (scRNA)-sequencing and its derivatives have revealed the heterogeneity present across cells within a given tissue.
The gap that remains is making sense of the variation observed through functional explanations: which cell states, regulatory elements, and genes play causal roles in the initiation and progression of diseases? Answering these questions systematically has proven challenging. In this thesis, we pursue these questions by developing frameworks to identify key cell states enriched for the heritability of disease, the role that clonality might play in the development of these cell states, and the gene regulatory networks at play within individual cell states of interest. We focus on immune-mediated diseases; however, our frameworks can be expanded more broadly.
We first leveraged multimodal single-nucleus RNA- and ATAC-seq data from inflamed synovial tissue to identify accessible regions of chromatin associated with distinct immune cell states. For 14 autoimmune diseases, we discovered that cell-state-dependent peaks in immune cell types assayed in inflammatory tissues disproportionately captured heritability and pointed to T peripheral helper, regulatory T, dendritic, and STAT1+CXCL10+ myeloid cell states as enriched for disease-critical genetic variation. These same populations are also expanded in inflammatory tissues. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.
We next sought to better understand which cell states may be clonally driven in immune-mediated responses. Thus, we tested for associations between T cell states important in immune response to infection and clonality in a COVID-19 dataset of >110,000 cells with scRNA- and scTCR-seq information to characterize which cell states—both discrete and continuous—may be driven by clonal membership. We further asked which specific clonotypes were explaining the variance for these cell states.
Finally, we presented a theoretical framework to systematically identify regulatory relationships within a cell state of interest without need for perturbation. Through modeling and simulations, we showed that this is possible by leveraging the intrinsic stochasticity in transcriptional bursting across individual cells at steady state. Importantly, changing magnitude of time-shifted correlations in RNA expression make it possible to distinguish covariation due to regulatory relationships within a cell state from covariation due to confounding from the presence of multiple cell states.
Overall, this work demonstrates the power of combining single-cell functional measurements with orthogonal information—from genetics to clonality to time—to identify disease-driving cell states and their associated regulatory networks.Medical SciencesMedical Science
Graph-theoretic approaches to biochemical reaction networks
No full textThis dissertation examines ways in which graph theory can unravel biochemical reaction networks. The many processes that occur in cells contribute to an overwhelming degree of molecular complexity. Representing biomolecular processes with graphs provides us with mathematical tools to reduce this complexity. At the same time, focusing on the graph's structure in terms of topology and geometry reveals unexpected behaviors that emerge from the underlying biological system. In this dissertation, we examine this interplay through three stand-alone chapters. The first two chapters utilize the linear framework, a graph-theoretic approach to time-scale separation in biochemical systems. The third chapter uses chemical reaction network theory (CRNT), which is based on systems of nonlinear ODEs derived from chemical reactions. While the linear framework and CRNT are both based on finite, directed graphs, the two methods are distinct, as discussed in the Introduction (Chapter 0).
A major success of the linear framework has been in its thermodynamic interpretation. Chapter 1 explores this area by updating and analyzing the method for detecting departure from thermodynamic equilibrium proposed by I.Z. Steinberg. The findings presented here demonstrate the signature’s anomalous behavior as a 3-vertex system is driven from thermodynamic equilibrium, particularly in the asymptotic analysis of the spectrum of its Laplacian matrix. Chapter 2 extends the mathematical foundations of the linear framework by introducing a novel graph-theoretic construction. This construction mimics what would happen if a single parameter in a graph is taken to infinity, producing an asymptotic graph. Characterizing this construction offers a potential formalization for a biological process that could happen instantaneously. Chapter 3 shifts gears from the linear framework and uses the machinery of CRNT. This work focuses on disguised topic dynamical systems, which are systems which exhibit a particularly stable dynamics but lack the typical structural features or algebraic constraints on the rate constants. This work, broadly speaking, interrogates how network geometry and topology influence qualitative dynamics of biochemical systems under the assumption of mass-action kinetics
Enabling Consumers to Adopt Reusable Packaging Systems in FMCG Products: An Application of the Behavior Change Wheel and Customer Journey Mapping
No full textFast-moving consumer goods (FMCG) products contribute to plastic waste since they are designed for single use and disposal, and reusable packaging is part of the solution to eliminate plastic waste pollution (EMF, 2019). Reusable packaging systems (RPS) can help reduce single-use packaging and transition to circular consumption, but their success depends on new business models and innovations. Incumbent and challenger companies have different approaches to implementing RPS due to their size, operations reach, ability to embed sustainability in their business models, and agility to change to incorporate circularity. However, a common challenge is that low consumer adoption of reusable packaging is a key barrier for companies to offer more RPS.
How are businesses helping consumers adopt sustainable consumption behaviors such as RPS? Most research has focused on consumers’ barriers to adopting RPS and on applying information and technology tools to support consumer purchasing decisions. However, it remains unclear how businesses can improve the overall customer experience of RPS, which is key to enabling reuse behaviors, increasing consumer demand, and achieving scalability. A key aim of this thesis was to examine and highlight the ways FMCG businesses incorporated effective interventions in the customer journey to allow consumers to adopt reusable packaging and contribute to reducing plastic pollution.
My research compared how incumbent and challenger companies influence consumer behavior in their customer journey design. For the sample, I selected 10 incumbent and 10 challenger RPS products sold online in the UK. The reuse models in scope are refill at home, return from home, and return on the go, as those can be purchased online. This research applied a novel approach that involved creating a scorecard using a combination of customer journey mapping (CJM) and the behavior change wheel (BCW) framework, which was used to evaluate how companies enable consumers to switch to their RPS products.
The scorecard results showed that RPS products from challenger companies performed better than RPS from incumbent companies. The products were then evaluated according to five factors that influence RPS consumer engagement: Understanding of RPS benefits, convenience, affordability, hygiene, and infrastructure accessibility. Most products addressed the understanding of the RPS benefits and convenience. However, gaps in the CJM included affordability, hygiene, and infrastructure accessibility. Additionally, findings showed that companies mainly applied RPS interventions in the pre-purchase and purchase stages of the customer journey. In contrast, fewer interventions were used in the post-purchase stage, which could hinder consistent RPS product adoption. Regarding the BCW, results show that not all the COM-B components of capability, opportunity, and motivation are addressed in the sample products through the interventions found in the CJM and surprisingly, motivation was the least addressed area. Finally, across the 35 interventions identified in the CJM, the most addressed intervention functions are education (30%), enablement (22%) and persuasion (15%).
This thesis concludes with a guideline for companies to evaluate their RPS offerings and identify improvement areas. The results of this work contribute to research on the effective implementation and scalability of RPS in the transition to a circular economy and can help businesses identify how to improve the customer journey design
Constructing a Spatially Resolved Single-Cell Reference Atlas of the Murine Gastrointestinal Tract with MERFISH
No full textThis dissertation investigates the advancement and application of imaging-based spatial transcriptomic techniques, specifically MERFISH, to high-RNase, cell-dense tissues such as the gastrointestinal (GI) tract. The study addressed critical experimental and computational challenges in RNA integrity, data quality control, as well as cell segmentation, and produced an all-cell-type, multi-region, spatially resolved single-cell reference atlas for both the specific- pathogen-free (SPF) and germ-free (GF) gut for the purpose of understanding gut cell type and spatial organization, small molecule sensation, and microbial interaction.
Chapter One: Introduction
The introductory chapter outlines the interdisciplinary field of imaging-based spatial transcriptomics, data processing, and spatial analysis methods within the context of gut biology and the microbiome. It identifies key technical challenges in RNA integrity, data quality control, and cell segmentation, as well as open biological inquiries regarding gut cell type organization, spatial niches and gradients, and the remodeling that happens in the absence of the microbiome. Among the discussion of technical challenges, a specific focus is given to comparing recent computational algorithms for cell segmentation in imaging-based spatial transcriptomic datasets.
Chapter Two: Technological Advancements to MERFISH
This chapter focuses on the technological hurdles encountered when applying MERFISH to the GI tract, a tissue characterized by high RNase activity and cellular density. Adjustments to existing brain MERFISH protocols were necessary to preserve RNA integrity in fresh frozen gut tissues. A novel data quality filtering approach was developed and benchmarked against traditional threshold-based methods. The limitations of existing cell segmentation techniques were addressed by enhancing the Baysor algorithm, incorporating Cellpose priors, and refining downstream processing steps such as low confidence RNA removal and doublet score screening. The qualitative, semi-quantitative, and quantitative metrics for assessing segmentation quality are discussed in this chapter, together with recommendations for tuning Baysor parameters for optimal segmentation performance.
Chapter Three: A Spatially Resolved, Single-Cell Atlas of The Gastrointestinal Tract
The MERFISH gut atlas revealed significant insights into the spatial organization and sensory capabilities of the mouse lower digestive tract. By constructing a spatially resolved single-cell atlas, the study identified both expected and novel cell types, their spatial distributions across four lower GI regions, and the receptor expression gradients that drive spatial heterogeneity. The absence of the microbiome was shown to selectively remodel these spatial features, highlighting the dynamic interplay between gut cells and microbial populations. These findings offer a comprehensive view of the molecular and cellular organization of gut sensation as well as microbial interaction, with potential pharmacological implications.
Chapter Four: Conclusions
The concluding chapter summarizes the research achievements of this Ph.D. work, emphasizing the technological advancements in MERFISH application to cell-dense, high-RNase tissues and the biological discoveries that enhance our understanding of gut organization and function
Quantifying Green House Gas (GHG) Emissions for Small Businesses in the US: Effective Policies to Reduce GHG
No full textSmall to medium sized enterprises (SMEs) make up 99% of businesses in the United States (US) and account for almost half of GDP. Collectively, SMEs play a large role in the economy and should play a large role in sustainability as well. SMEs might consume more energy than corporations and have the potential to reduce more greenhouse gases. Even though there has been growing pressure on corporations to reduce greenhouse gas (GHG) emissions, SMEs have not been included in the sustainability conversation, perhaps because it’s been difficult to include them, or due to the perception of their irrelevance. Governments and investors have instead put pressure on corporations, which also have multiple reporting tools, tax breaks and incentives to help urge them to participate. It’s hard for SMEs to engage in sustainability initiatives because they can be costly and require time and human resources. Because few small businesses are engaged in sustainability efforts, there is little information on how they contribute to climate change.
This research aimed to quantify GHG emissions from SMEs in the US and evaluate future GHG emissions and reductions based on a variety of scenarios by addressing four main questions: What percentage of GHG emissions can be attributed to SMEs in the US? If every SME incrementally reduced GHG emissions, how much change would be seen? How much GHG can be avoided under different incentive scenarios? And what policies and incentives are available to motivate SMEs to participate? I aimed to test these hypotheses: 1) If small businesses in America reduced greenhouse gas emissions by 10%, total GHG emissions in the US would be reduced by 1 billion metric tons (t) per year, equivalent to 217 million cars (75% of cars on the road in the US). 2) A financial incentive to achieve LEED or ENERGY STAR certifications could be implemented to avoid 500 million t carbon dioxide equivalents (CO2e).
The Commercial Buildings Energy Consumption Survey (CEBCS) was used to identify building energy consumption. These data were matched with regional power grids, emission factors and global warming potential, and calculated into metric tons (t) CO2e emissions. It was determined that SMEs in the U.S. contribute to climate change by emitting 272 million t of CO2e per year, more than Thailand, the Philippines or Spain, and is responsible for 33% of all commercial building emissions in the US. Over the course of ten years, SMEs will emit almost 3 billion t CO2e just in building emissions.
A variety of scenarios were analyzed to show how measures that can be taken would affect cumulative GHG emissions through the year 2050. The scenario that included building energy efficiencies, consuming carbon free energy and eliminating the use of natural gas and fuel oil indicated that zero emissions could be achieved before 2050.
To motivate change, a synergistic mix of reporting tools to benchmark, incentives to motivate, regulatory requirements to drive performance, and financial assistance to cover upfront costs where necessary are needed. Some of the methods that could be used include incentivizing LEED or ENERGY STAR certification, carbon taxes or caps, and GHG trading systems. If the global goal of “net zero” emissions by 2050 is to be achieved, this small, but collectively large player is essential to include
Institutions and cooperation: The cultural evolutionary roots of prosociality in Oaxaca, Mexico
No full textAlthough humans are capable of remarkable feats of cooperation, failures of cooperation lie at the root of many of humanity’s biggest challenges, from managing common resources to combating the spread of infectious disease. Moreover, there is puzzling variation between groups in when people cooperate, how intensely, and with whom. Why do we see this variation? In this dissertation, I combine cultural evolutionary theory with methods from anthropology, psychology, and economics to examine how institutions– packages of social norms– structure prosocial psychology and cooperation within groups.
Chapter 1 begins by establishing a theoretical framework for understanding how culturally evolved institutions harness psychological and social mechanisms to stabilize cooperation. Then, I put this framework to the test at my fieldsite in a Zapotec village of Oaxaca, Mexico. Drawing on qualitative and quantitative data from participant observation, interviews, surveys, and vignettes, I dissect two of the village’s cooperative institutions, uncovering the mechanisms through which they foster mutual aid and collective action. I find that these institutions are governed by social norms that drive domain-specific cooperation; they are not associated with generalized prosociality. Moreover, the institutions rely on overlapping but distinct sets of psychological and social mechanisms to stabilize cooperation. These results suggest that as institutions culturally evolve, they can stitch together different cooperation-sustaining mechanisms– elucidating the rich diversity of culturally evolved institutions.
Chapter 2 focuses on usos y costumbres, a set of traditional political institutions by which indigenous Oaxacan communities self-govern. Leveraging secondary and newly coded ethnographic survey data from 418 Oaxacan municipalities, I show that communities with stronger usos y costumbres institutions mobilize more cooperation for the group benefit– consistent with a cultural evolutionary view of cooperation.
In Chapter 3, I return to my fieldsite to examine another institution, fiestas– elaborate, multiday festivals that honor patron saints. Positing that fiestas function as cohesion-enhancing collective rituals, I compare prosocial attitudes and cooperation in a behavioral economics game several months before and immediately after the village’s biggest annual fiesta. Results do not support the hypothesis, instead revealing declines in ingroup altruism and cooperation, and no change in cohesion. At the same time, data indicate that the fiesta itself is a massive cooperative undertaking for the community, complicating interpretation of the data.
Taken together, these studies provide novel insights into the evolution of human cooperation, elucidating the role of culturally evolved institutions
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
No full textAlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the model’s generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2. We train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFold’s capacity to generalize across fold space by retraining it using carefully designed datasets. We find that OpenFold is remarkably robust at generalizing despite extreme reductions in training set size and diversity, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced by OpenFold during training, we also gain surprising insights into the manner in which the model learns to fold proteins, discovering that spatial dimensions are learned sequentially. Taken together, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial new resource for the protein modeling community.Accepted Manuscrip
Logical quantum processor based on reconfigurable atom arrays
No full textSuppressing errors is the central challenge for useful quantum computing1, requiring quantum error correction (QEC)2–6 for large-scale processing. However, the overhead in the realization of error-corrected 'logical' qubits, in which information is encoded across many physical qubits for redundancy2–4, poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays7, our system combines high two-qubit gate fidelities8, arbitrary connectivity7,9, as well as fully programmable single-qubit rotations and mid-circuit readout10–15. Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities5, fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks16,17, we realize computationally complex sampling circuits18 with up to 48 logical qubits entangled with hypercube connectivity19 with 228 logical two-qubit gates and 48 logical CCZ gates20. We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling21,22. These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors.Version of Recor
Cybersecurity Features of Digital Medical Devices: An Analysis of FDA Product Summaries
No full textObjectives: To more clearly define the landscape of digital medical devices subject to US Food and Drug Administration (FDA) oversight, this analysis leverages publicly available regulatory documents to characterise the prevalence and trends of software and cybersecurity features in regulated medical devices. Design: We analysed data from publicly available FDA product summaries to understand the frequency and recent time trends of inclusion of software and cybersecurity content in publicly available product information. Setting: The full set of regulated medical devices, approved over the years 2002–2016 included in the FDA’s 510(k) and premarket approval databases. Primary and secondary outcome measures: The primary outcome was the share of devices containing software that included cybersecurity content in their product summaries. Secondary outcomes were differences in these shares (a) over time and (b) across regulatory areas. Results: Among regulated devices, 13.79% were identified as including software. Among these products, only 2.13% had product summaries that included cybersecurity content over the period studied. The overall share of devices including cybersecurity content was higher in recent years, growing from an average of 1.4% in the first decade of our sample to 5.5% in 2015 and 2016, the most recent years included. The share of devices including cybersecurity content also varied across regulatory areas from a low of 0% to a high of 22.2%. Conclusions: To ensure the safest possible healthcare delivery environment for patients and hospitals, regulators and manufacturers should work together to make the software and cybersecurity content of new medical devices more easily accessible.Version of Recor
The road from thought to action: a brainwide atlas of spinal projecting neurons
No full textThe brain executes control of nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from numerous supraspinal regions to the spinal cord. Despite significant progress in identifying the major anatomical tracts and their functions, the molecular and cellular mechanisms underlying SPN connectivity and function remain unknown. In this dissertation, using retrograde labeling, whole-brain imaging, and high-throughput transcriptional profiling, I generated a unified brain-wide anatomic and transcriptomic atlas of adult mouse SPNs at single-cell resolution. This atlas revealed important insights for understanding the anatomic (Chapter 1), transcriptomic (Chapter 2), and electrophysiologic (Chapter 3) features of these neurons:
Chapter 1 provides insights into the anatomical distribution of cervical- and lumbar-projecting SPNs via serial two-photon tomography and registration to the Allen Mouse Common Coordinate Framework. This work comprehensively mapped the distribution of SPNs, confirming concentrations in the cortex, hypothalamus, midbrain, cerebellum, pons, and medulla.
Chapter 2 showcases a multi-level transcriptomic taxonomy developed using single-nucleus transcriptomic profiling of 65,002 SPNs. This taxonomy revealed a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs in the cortex, red nucleus, and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) highly heterogeneous excitatory and inhibitory populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands to the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain, and reticular formation for gain control of brain-spinal signals.
Chapter 3 focuses on the electrophysiological properties of SPNs. Cell-attached and whole-cell recordings of retrogradely labeled SPNs revealed electrophysiological features of a subset of SPNs that highly express transcriptional signatures correlating with fast-firing properties (namely, Pvalb/Kcng4/Spp1). These results show that Spp1+ SPNs are defined by fast-conducting properties, suggesting the presence of different cable lines (i.e., “fast and slow”) transmitting brain signals to the spinal cord.
This body of work is the first to systematically describe brain-wide spinal projecting neurons by integrating their anatomic, transcriptomic, and electrophysiologic features, promising to deepen our understanding of how the brain controls the body, and may contribute to the development of new therapeutic approaches for disorders affecting descending pathways