This article examines how scientists move from physical measurementsto actual observation of single-cell recordings in the brain. We highlight how easy it is to change the fundamental nature of ourobservations using accepted methodological techniques for manipulatingraw data. Collecting single-cell data is thoroughly pragmatic. Weconclude that there is no deep or interesting difference betweenaccounting for observations by measurements and accounting forobservations by theories.

Various techniques have attempted to localize imagery. However, early findings using single cell recordings of human receptive fields during imagery tasks have had little impact. Reports by Marg and his coworkers (1968) found no evidence for imagery in human Area 17, 18, and 19. Single cells from humans suggest later imagery-related activity in hippocampus, amygdala, entorhinal cortex, and parahippocampal gyrus.

A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...) in connectionist models and neural coding in brain and often dismiss localist theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models. (shrink)

Single cell recordings in monkeys provide strong evidence for an important role of the motor system in action understanding. This evidence is backed up by data from studies of the (human) mirror neuron system using neuroimaging or TMS techniques, and behavioral experiments. Although the data acquired from single cell recordings are generally considered to be robust, several debates have shown that the interpretation of these data is far from straightforward. We will show that research (...) based on single-cell recordings allows for unlimited content attribution to mirror neurons. We will argue that a theoretical analysis of the mirroring process, combined with behavioral and brain studies, can provide the necessary limitations. A complexity analysis of the type of processing attributed to the mirror neuron system can help formulate restrictions on what mirroring is and what cognitive functions could, in principle, be explained by a mirror mechanism. We argue that processing at higher levels of abstraction needs assistance of non-mirroring processes to such an extent that subsuming the processes needed to infer goals from actions under the label ?mirroring? is not warranted. (shrink)

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, (...) class='Hi'>single-cell recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

The concept of the cortical column refers to vertical cell bands with similar response properties, which were initially observed by Vernon Mountcastle’s mapping of single cell recordings in the cat somatic cortex. It has subsequently guided over 50 years of neuroscientific research, in which fundamental questions about the modularity of the cortex and basic principles of sensory information processing were empirically investigated. Nevertheless, the status of the column remains controversial today, as skeptical commentators proclaim that the (...) vertical cell bands are a functionally insignificant by-product of ontogenetic development. This paper inquires how the column came to be viewed as an elementary unit of the cortex from Mountcastle’s discovery in 1955 until David Hubel and Torsten Wiesel’s reception of the Nobel Prize in 1981. I first argue that Mountcastle’s vertical electrode recordings served as criteria for applying the column concept to electrophysiological data. In contrast to previous authors, I claim that this move from electrophysiological data to the phenomenon of columnar responses was concept-laden, but not theory-laden. In the second part of the paper, I argue that Mountcastle’s criteria provided Hubel Wiesel with a conceptual outlook, i.e. it allowed them to anticipate columnar patterns in the cat and macaque visual cortex. I argue that in the late 1970s, this outlook only briefly took a form that one could call a ‘theory’ of the cerebral cortex, before new experimental techniques started to diversify column research. I end by showing how this account of early column research fits into a larger project that follows the conceptual development of the column into the present. (shrink)

We contrast reactive and endogenously active perspectives on brain activity. Both have been pursued continuously in neurophysiology laboratories since the early 20thcentury, but the endogenous perspective has received relatively little attention until recently. One of the many successes of the reactive perspective was the identification, in the second half of the 20th century, of the distinctive contributions of different brain regions involved in visual processing. The recent prominence of the endogenous perspective is due to new findings of ongoing oscillatory activity (...) in the brain at a wide range of time scales, exploiting such techniques as single-cell recording, EEG, and fMRI. We recount some of the evidence pointing to ways in which this endogenous activity is relevant to cognition and behavior. Our major objective is to consider certain implications of the contrast between the reactive and endogenous perspectives. In particular, we relate these perspectives to two different characterizations of explanation in the new mechanistic philosophy of science. In a basic mechanistic explanation, the operations of a mechanism are characterized qualitatively and as functioning sequentially until a terminating condition is realized. In contrast, a dynamic mechanistic explanation allows for non-sequential organization and emphasizes quantitative modeling of the mechanisms's behavior. For example, with appropriate parameter values a set of differential equations can be used to demonstrate ongoing oscillations in a system organized with feedback loops. We conclude that the basic conception of mechanistic explanation is adequate for reactive accounts of brain activity, but dynamical accounts are required to explain sustained, endogenous activity. (shrink)

There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every (...) level. This sentiment, however, is much less congenial to the tried‐and‐true experimental protocols available today and to theorizing about the brain in general. This essay examines the tension between current experimental methods and large-scale views of the brain. We argue that this disconnection between experiment and what really are guiding theoretical metaphors seriously impedes progress in neuroscience. (shrink)

There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every (...) level. This sentiment, however, is much less congenial to the tried‐and‐true experimental protocols available today and to theorizing about the brain in general. This essay examines the tension between current experimental methods and large-scale views of the brain. We argue that this disconnection between experiment and what really are guiding theoretical metaphors seriously impedes progress in neuroscience. (shrink)

If we assume that consciousness is a natural biological phenomenon in the brain, should we expect the current brain sensing and imaging methods to somehow ‘discover’ consciousness? The answer depends on the following points: What kind of level of biological organization do we assume consciousness to be? What would count as the discovery of this level? What are the levels of organization from which the currently available research instruments pick signals and acquire data? Single-cell recordings, PET, fMRI, (...) EEG and MEG pick different types of signals from different levels of organization in the brain. However, it seems they do not manage to pick signals that would allow the direct visualization and reconstruction of the higher levels of electrophysiological organization that are crucial for the empirical discovery and theoretical explanation of consciousness. The message of the present paper is twofold: On the one hand, we should be aware of the practical limitations of the currently available methods of cognitive neuroscience and not read too much into the images produced by them. On the other hand, the present limitations could be overcome by more sophisticated methods in the future. Therefore, contrary to what several philosophers have argued, the empirical discovery of consciousness in the brain is not impossible in principle. (shrink)

Results from three experiments using spatial forced-choice techniques show that an illusory contour improves the detectability of a spatially superimposed, 1pixel-thin subthreshold line of either contrast polarity. Furthermore, the subthreshold line is found to enhance the visibility of an illusory contour bridging the gap between the two colinear edges of physically defined boundaries. Stimuli which do not induce illusory contours, but reduce uncertainty about the spatial position of the line, give rise to a slight detection facilitation, but the threshold of (...) 75% correct responses is not attained. The data indicate that superimposing illusory contours and 1pixel thin subthreshold lines produces interactions which are similar to classic subthreshold summation. This provides psychophysical evidence for the functional equivalence of illusory and real lines, as suggested by neurophysiological data from single-cell recordings in V1 and V2 of cats and primates. (shrink)

Both classical psychophysical work and recentfunctional imaging studies have suggested acritical role for the primary visual cortex(V1) in resolving the perceptual ambiguitiesexperienced during binocular rivalry. Here weexamine, by means of single-cell recordings andoptical imaging of intrinsic signals, thespatial characteristics of suppression elicitedby rival stimuli in cat V1. We find that the interocular suppression field of V1 neuronsis centred on the same position in space and isslightly larger (by a factor of 1.3) than theminimum response field, measured through (...) thesame eye. Suppression is always strongest at asingle position corresponding very closely tothe centre of the classical receptive field,and reduces responses through the other eye byup to 90% but typically around 40%. Thespatial pattern of interocular suppression, asrevealed by optical imaging, closely matchesthe cortical representation of the stimulus,which is being suppressed, both in terms of itsorientation and the eye of origin. Theseresults indicate that interocular suppressionis directly related to the functionalarchitecture of V1; it is probably caused bydirect inhibitory interactions betweenneighbouring cortical columns of oppositeocular dominance. (shrink)

Cerebellar Purkinje cells generate two distinct types of spikes, complex and simple spikes, both of which have conventionally been considered to be highly irregular, suggestive of certain types of stochastic processes as underlying mechanisms. Interestingly, however, the interspike interval structures of complex spikes have not been carefully studied so far. We showed in a previous study that simple spike trains are actually composed of regular patterns and single interspike intervals, a mixture that could not be explained by a simple (...) rate-modulated Poisson process. In the present study, we systematically investigated the interspike interval structures of separated complex and simple spike trains recorded in anaesthetized rats, and derived an appropriate stochastic model. We found that: (i) complex spike trains do not exhibit any serial correlations, so they can effectively be generated by a renewal process, (ii) the distribution of intervals between complex spikes exhibits two narrow bands, possibly caused by two oscillatory bands (0.5–1 and 4–8 Hz) in the input to Purkinje cells and (iii) the regularity of regular patterns and single interspike intervals in simple spike trains can be represented by gamma processes of orders, which themselves are drawn from gamma distributions, suggesting that multiple sources modulate the regularity of simple spike trains. (shrink)

Despite the importance of mathematics in our educational systems little is known about how abstract mathematical thinking emerges. Under the uniting thread of mathematical development, we hope to connect researchers from various backgrounds to provide an integrated view of abstract mathematical cognition. Much progress has been made in the last 20 years on how numeracy is acquired. Experimental psychology has brought to light the fact that numerical cognition stems from spatial cognition. The findings from neuroimaging and single cell (...) recording experiments converge to show that numerical representations take place in the intraparietal sulcus. Further research has demonstrated that supplementary neural networks might be recruited to carry out subtasks; for example, the retrieval of arithmetic facts is done by the angular gyrus. Now that the neural networks in charge of basic mathematical cognition are identified, we can move onto the stage where we seek to understand how these basics skills are used to support the acquisition and use of abstract mathematical concepts. (shrink)

Neurophysiological investigations of the visual system by way of single-cell recordings have revealed a hierarchical architecture in which lower level areas, such as the primary visual cortex, contain cells that respond to simple features, while higher level areas contain cells that respond to higher order features apparently composed of combinations of lower level features. This architecture seems to suggest a feed-forward processing strategy in which visual information progresses from lower to higher visual areas. However there is other (...) evidence, both neurophysiological and phenomenal, that suggests a more parallel processing strategy in biological vision, in which top-down feedback plays a significant role. In fact Gestalt theory suggests that visual perception involves a process of emergence, i.e. a dynamic relaxation of multiple constraints throughout the system simultaneously, so that the final percept represents a stable state, or energy minimum of the dynamic system as a whole. A Multi-Level Reciprocal Feedback (MLRF) model is proposed to resolve the apparently contradictory concepts, by proposing a hierarchical visual architecture whose different levels are connected by bi-directional feed-forward and feedback pathways, where the computational transformation performed by the feedback pathway between levels in the hiararchy is a kind of inverse of the transformation performed by the corresponding feed-forward processing stream. This alternative paradigm of perceptual computation accounts in general terms for a number of visual illusory effects, and offers a computational specification for the generative, or constructive aspect of perceptual processing revealed by Gestalt theory. (shrink)

The target article retains the traditional account of saccades as conjugate eye movements. However, recent single-unit recordings of premotor cells in the saccade pathway (excitatory burster neurons [EBNs]) found that they do not encode conjugate eye velocity, but rather, monocular eye velocity. These data argue against the traditional concept of saccades as inherently conjugate. Instead, they suggest a monocular mechanism in the sensorimotor transformation stage of saccade generation. This commentary will discuss the implications of these data for the (...) saccade generation model proposed in Findlay & Walker's target article. (shrink)

Single‐cell microinjection has been successfully used to deliver exogenous proteins, cDNA constructs, peptides, drugs and particles into transfection‐challenged cells. With precisely controlled delivery dosage and timing, microinjection has been used in many studies of primary cultured cells, transgenic animal production, in vitro fertilization and RNA inference. This review discusses the advantages and limits of microinjection as a mechanical delivery method and its applications to attached and suspended cells. BioEssays 30:606–610, 2008. © 2008 Wiley Periodicals, Inc.

The nature of attention is one of the oldest and most central problems in psychology. A huge amount of research has been produced on this subject in the last half century, especially on attention in the visual modality, but a general explanation has remained elusive. Many still view attention research as a field that is fundamentally fragmented. This book takes a different perspective and presents a unified theory of visual attention: the TVA model. The TVA model explains the many aspects (...) of visual attention by just two mechanisms for selection of information: filtering and pigeonholing. These mechanisms are described in a set of simple equations, which allow TVA to mathematically model a large number of classical results in the attention literature. The theory explains psychological and neuroscientific findings by the same equations; TVA is a complete theory of visual attention, linking mind and brain. Aimed at advanced students and professional researchers, Principles of Visual Attention contains a detailed review of the most important research done on attention in vision, spanning cognitive psychology, brain imaging, patient studies, and recordings from single cells in the visual cortex. The book explains the TVA model and shows how it accounts for attentional effects observed across all the research areas described. Principles of Visual Attention offers a uniquely integrated view on a central topic in cognitive neuroscience. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On (...) the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

Recent years have witnessed an explosion of the single-cell biochemical toolbox including chromosome conformation capture -based methods that provide novel insights into chromatin spatial organization in individual cells. The observations made with these techniques revealed that topologically associating domains emerge from cell population averages and do not exist as static structures in individual cells. Stochastic nature of the genome folding is likely to be biologically relevant and may reflect the ability of chromatin fibers to adopt a number (...) of alternative configurations, some of which could be transiently stabilized and serve regulatory purposes. Single-cell Hi-C approaches provide an opportunity to analyze chromatin folding in rare cell types such as stem cells, tumor progenitors, oocytes, and totipotent cells, contributing to a deeper understanding of basic mechanisms in development and disease. Here, we review key findings of single-cell Hi-C and discuss possible biological reasons and consequences of the inferred dynamic chromatin spatial organization. Single-cell Hi-C expands the molecular biology toolbox for studies of chromatin structure in individual cells. This technique allows one to analyze cell-to-cell variability in TAD and loop structure, and to capture chromosome conformation in rare cell types such as oocytes, totipotent cells, and tumor progenitors. (shrink)

Recent years have witnessed an explosion of the single-cell biochemical toolbox including chromosome conformation capture -based methods that provide novel insights into chromatin spatial organization in individual cells. The observations made with these techniques revealed that topologically associating domains emerge from cell population averages and do not exist as static structures in individual cells. Stochastic nature of the genome folding is likely to be biologically relevant and may reflect the ability of chromatin fibers to adopt a number (...) of alternative configurations, some of which could be transiently stabilized and serve regulatory purposes. Single-cell Hi-C approaches provide an opportunity to analyze chromatin folding in rare cell types such as stem cells, tumor progenitors, oocytes, and totipotent cells, contributing to a deeper understanding of basic mechanisms in development and disease. Here, we review key findings of single-cell Hi-C and discuss possible biological reasons and consequences of the inferred dynamic chromatin spatial organization. Single-cell Hi-C expands the molecular biology toolbox for studies of chromatin structure in individual cells. This technique allows one to analyze cell-to-cell variability in TAD and loop structure, and to capture chromosome conformation in rare cell types such as oocytes, totipotent cells, and tumor progenitors. (shrink)

Overview The evolution of multicellularity raises questions regarding genomic and developmental commonalities and discordances, selective advantages and disadvantages, physical determinants of development, and the origins of morphological novelties. It also represents a change in the definition of individuality, because a new organism emerges from interactions among single cells. This volume considers these and other questions, with contributions that explore the origins and consequences of the evolution of multicellularity, addressing a range of topics, organisms, and experimental protocols. Each section focuses (...) on selected topics or particular lineages that present a significant insight or challenge. The contributors consider the fossil record of the paleontological circumstances in which animal multicellularity evolved; cooptation, recurrent patterns, modularity, and plausible pathways for multicellular evolution in plants; theoretical approaches to the amoebozoa and fungi (cellular slime molds having long provided a robust model system for exploring the evolution of multicellularity), plants, and animals; genomic toolkits of metazoan multicellularity; and philosophical aspects of the meaning of individuality in light of multicellular evolution. Contributors Maja Adamska, Argyris Arnellos, Juan A. Arias, Eugenio Azpeitia, Mariana Benítez, Adriano Bonforti, John Tyler Bonner, Peter L. Conlin, A. Keith Dunker, Salva Duran-Nebreda, Ana E. Escalante, Valeria Hernández-Hernández, Kunihiko Kaneko, Andrew H. Knoll, Stephan G. König, Daniel J. G. Lahr, Ottoline Leyser, Alan C. Love, Raul Montañez, Emilio Mora van Cauwelaert, Alvaro Moreno, Vidyanand Nanjundiah, Aurora M. Nedelcu, Stuart A. Newman, Karl J. Niklas, William C. Ratcliff, Iñaki Ruiz-Trillo, Ricard Solé . (shrink)

Revonsuo makes a provocative and interesting claim: that currently available neurophysiological recording techniques will be unable to discover the neural basis of consciousness in the brain. Although the title refers exclusively to functional brain imaging, Revonsuo considers MEG, EEG, ERP and measurements of firing rate in single cell electrophysiology all in principle incapable of discovering consciousness in the brain. This conclusion is reached by assuming that only one particular type of physical entity constitutes awareness.

I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms (...) of teleofunction, explicated in terms of natural selection. -/- To explain (ii), we begin by recognizing that representational states do not have content, that is, they are neither true nor false except insofar as they both “point to” or “refer” to something, as well as “say” something regarding whatever it is they are about. To distinguish veridical from false representations, there must be a way for these separate aspects to come apart; hence, we explain (ii) by providing independent theories of what I call f-reference and f-predication (the ‘f’ simply connotes ‘fundamental’, to distinguish these things from their natural language counterparts). -/- Causal theories of representation typically founder on error, or on what Fodor has called the disjunction problem. Resemblance or isomorphism theories typically founder on what I’ve called the non-uniqueness problem, which is that isomorphisms and resemblance are practically unconstrained and so representational content cannot be uniquely determined. These traditional problems provide the motivation for my theory, the structural preservation theory, as follows. F-reference, like reference, is a specific, asymmetric relation, as is causation. F-predication, like predication, is a non-specific relation, as predicates typically apply to many things, just as many relational systems can be isomorphic to any given relational system. Putting these observations together, a promising strategy is to explain f-reference via causal history and f-predication via something like isomorphism between relational systems. -/- This dissertation should be conceptualized as having three parts. After motivating and characterizing the problem in chapter 1, the first part is the negative project, where I review and critique Dretske’s, Fodor’s, and Millikan’s theories in chapters 2-4. Second, I construct my theory about the nature of representation in chapter 5 and defend it from objections in chapter 6. In chapters 7-8, which constitute the third and final part, I address the question of how representation is implemented in biological systems. In chapter 7 I argue that single-cell intracortical recordings taken from awake Macaque monkeys performing a cognitive task provide empirical evidence for structural preservation theory, and in chapter 8 I use the empirical results to illustrate, clarify, and refine the theory. (shrink)

Head direction (HD) cells, abundant in the rat postsubiculum and anterior thalamic nuclei, fire maximally when the rat’s head is facing a particular direction. The activity of a population of these cells forms a distributed representation of the animal’s current heading. We describe a neural network model that creates a stable, distributed representation of head direction and updates that representation in response to angular velocity information. In contrast to earlier models, our model of the head direction system accurately tracks a (...) series of actual rat head rotations, and, using biologically plausible neurons, it fits the single-cell tuning curves of real HD cells recorded from rats executing those same rotations. The model makes neurophysiological predictions that can be tested using current technologies. (shrink)

In the present study, we have demonstrated hysteresis phenomena in the excitability of single, enzymatically dissociated guinea pig ventricular myocytes. Membrane potentials were recorded with patch pipettes in the whole-cell current clamp configuration. Repetitive stimulation with depolarizing current pulses of constant cycle length and duration but varying strength led to predictable excitation (1:l) and non-excitation (1:0) patterns depending on current strength. In addition, transition between patterns depended on the direction of current intensity change and stable hysteresis loops were (...) obtained in stimulus:response pattern vs. current intensity plots in 14 cells. Increase of pulse duration and decrease of stimulation rate contributed to a reduction in hysteresis loop areas. Changes in amplitude and shape of the subthreshold responses during the transitions from one stable pattern to the other, suggested that activity led to an increase in membrane resistance, particularly in the voltage domain between resting potential, and threshold. Therefore, we modelled the dynamic behaviour of the single cells as a function of diastolic membrane resistance, using previously published analytical solutions. Numerical iteration of the analytical model equations closely reproduced the experimental hysteresis loops in both qualitative and quantitative ways. In particular, the effect of stimulation frequency on the model was similar to the experimental findings. The overall study suggests that the excitability pattern of guinea pig ventricular myocytes accounts for hysteresis and bistabilities when current intensity is allowed to fluctuate around threshold levels. (shrink)

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables (...) the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research. (shrink)

There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single‐cell molecular variation per se might be crucial for population‐level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of “identical” cells, and this functional diversity facilitates collective behavior that would be inaccessible to (...) a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population‐level function. We consider several possible methods to further pursue the hypothesis that “variation is function” through the use of comparative analysis and novel experimental techniques. (shrink)

Tissues contain complex populations of cells. Like countries, which are comprised of mixed populations of people, tissues are not homogeneous. Gene expression studies that analyze entire populations of cells from tissues as a mixture are blind to this diversity. Thus, critical information is lost when studying samples rich in specialized but diverse cells such as tumors, iPS colonies, or brain tissue. High throughput methods are needed to address, model and understand the constitutive and stochastic differences between individual cells. Here, we (...) describe microfluidics technologies that utilize a combination of molecular biology and miniaturized labs on chips to study gene expression at the single cell level. We discuss how the characterization of the transcriptome of each cell in a sample will open a new field in gene expression analysis, population transcriptomics, that will change the academic and biomedical analysis of complex samples by defining them as quantified populations of single cells. (shrink)

Persistent activity can be the product of mechanisms other than attractor reverberations. The single-unit data presented by Amit cannot discriminate between the different mechanisms. In fact, single-unit data do not appear to be adequate for testing neural network models.

A reductionistic, bottom‐up, cellular‐based concept of the origins of sentience and consciousness has been put forward. Because all life is based on cells, any evolutionary theory of the emergence of sentience and consciousness must be grounded in mechanisms that take place in prokaryotes, the simplest unicellular species. It has been posited that subjective awareness is a fundamental property of cellular life. It emerges as an inherent feature of, and contemporaneously with, the very first life‐forms. All other varieties of mentation are (...) the result of evolutionary mechanisms based on this singular event. Therefore, all forms of sentience and consciousness evolve from this original instantiation in prokaryotes. It has also been identified that three cellular structures and mechanisms that likely play critical roles here are excitable membranes, oscillating cytoskeletal polymers, and structurally flexible proteins. Finally, basic biophysical principles are proposed to guide those processes that underly the emergence of supracellular sentience from cellular sentience in multicellular organisms. (shrink)

The emergence of individuality during the evolutionary transition from single cells to multicellularity poses a range of problems. A key issue is how variation in lower‐level individuals generates a corporate (collective) entity with Darwinian characteristics. Of central importance to this process is the evolution of a means of collective reproduction, however, the evolution of a means of collective reproduction is not a trivial issue, requiring careful consideration of mechanistic details. Calling upon observations from experiments, we draw attention to proto‐life (...) cycles that emerge via unconventional routes and that transition, in single steps, individuality to higher levels. One such life cycle arises from conflicts among levels of selection and invokes cheats as a primitive germ line: it lays the foundation for collective reproduction, the basis of a self‐policing system, the selective environment for the emergence of development, and hints at a plausible origin for a soma/germ line distinction. (shrink)

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single‐cell level, so genetic epidemiological models need to be updated to integrate with single‐cell expression data. In this perspective paper, we propose a cell population‐based framework for genetic epidemiology in the single‐cell era. In this framework, genetic diversity influences phenotypic diversity through the (...) diversity of cell population profiles, which are defined as high‐dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single‐cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology. (shrink)

Bacterial populations are heterogeneous, which in many cases can provide a selective advantage during changes in environmental conditions. In some instances, heterogeneity exists at the genetic level, in which significant allelic variation occurs within a population seeded by a single cell. In other cases, heterogeneity exists due to phenotypic differences within a clonal, genetically identical population. A variety of mechanisms can drive this latter strategy. Stochastic fluctuations can drive differential gene expression, but heterogeneity in gene expression can also (...) be driven by environmental changes sensed by individual cells residing in distinct locales. Utilizing multiple single cell approaches, workers have started to uncover the extent of heterogeneity within bacterial populations. This review will first describe several examples of phenotypic and genetic heterogeneity, and then discuss many single cell approaches that have recently been applied to define heterogeneity within bacterial populations. (shrink)

Mapping the paths that stem and progenitor cells take en route to differentiate and elucidating the underlying molecular controls are key goals in developmental and stem cell biology. However, with population level analyses it is difficult − if not impossible − to define the transition states and lineage trajectory branch points within complex developmental lineages. Single‐cell RNA‐sequencing analysis can discriminate heterogeneity in a population of cells and even identify rare or transient intermediates. In this review, we propose (...) that using these data, one can infer the lineage trajectories of individual stem cells and identify putative branch points. Clonal lineage tracing of stem cells allows one to define the outcome of differentiation. Integrating these single cell‐based approaches provides a robust strategy for establishing and testing models of how an individual stem cell changes through time to differentiate and self‐renew. (shrink)

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene (...) expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development. (shrink)

Leading prescriptions for addressing the spread of fake news, misinformation, and other forms of epistemically toxic content online target either the platform or platform users as a single site for intervention. Neither approach attends to the intense feedback between people, posts, and platforms. Leading prescriptions boil down to the suggestion that we make social media more like traditional media, whether by making platforms take active roles as gatekeepers, or by exhorting individuals to behave more like media professionals. Both approaches (...) are impracticable and wrong. (shrink)

I have previously argued that stem cell experiments cannot demonstrate that a single cell is a stem cell (Fagan 2013a, b). Laplane and others dispute this claim, citing experiments that identify stem cells at the singlecell level. This paper rebuts the counterexample, arguing that the alleged ‘crucial stem cell experiments’ do not measure self-renewal for a single cell, do not establish a single cell’s differentiation potential, and, if interpreted as providing results (...) about single cells, fall into epistemic circularity. I then discuss the source of the dispute, locating it in differences between philosophical and experimental perspectives. (shrink)

I have previously argued that stem cell experiments cannot demonstrate that a single cell is a stem cell. Laplane and others dispute this claim, citing experiments that identify stem cells at the single-cell level. This paper rebuts the counterexample, arguing that the alleged ‘crucial stem cell experiments’ do not measure self-renewal for a single cell, do not establish a single cell’s differentiation potential, and, if interpreted as providing results about (...) single cells, fall into epistemic circularity. I then discuss the source of the dispute, locating it in differences between philosophical and experimental perspectives. (shrink)

A recent single cell mRNA sequencing study by Dueck et al. compares neuronal transcriptomes to the transcriptomes of adipocytes and cardiomyocytes. Single cell ‘omic approaches such as those used by the authors are at the leading edge of molecular and biophysical measurement. Many groups are currently employing single cell sequencing approaches to understand cellular heterogeneity in cancer and during normal development. These single cell approaches also are beginning to address long‐standing questions regarding (...) nervous system diversity. Beyond an innate interest in cataloging cell type diversity in the brain, single cell neuronal diversity has important implications for neurotypic neural circuit function and for neurological disease. Herein, we review the authors’ methods and findings, which most notably include evidence of unique expression profiles in some single neurons. (shrink)

This expands the proposal in 'Is consciousness only a property of individual cells?' to attempt to cover all relevant psychological, neuroscientific and philosophical issues. Some of the material is now dated (in 2011) but chiefly in the sense that tentative proposals have become firmer views for me. An example of this is the clarification of complementarities in "Are our spaces made of words?'.

Stochastic gene expression plays a leading developmental role through its contribution to cell differentiation. It is also proposed to promote phenotypic diversification in malignant cells. However, it remains unclear if these two forms of cellular bet‐hedging are identical or rather display distinct features. Here we argue that bet‐hedging phenomena in cancer cells are more similar to those occurring in unicellular organisms than to those of normal metazoan cells. We further propose that the atavistic bet‐hedging strategies in cancer originate from (...) a hijacking of the normal developmental bet‐hedging of metazoans. Finally, we discuss the constraints that may shape the atavistic bet‐hedging strategies of cancer cells. (shrink)