News & Publications
Epub 2022 Jul 18. Behavioral genetics and genomics: Mendel's peas, mice, and bees
The question of the heritability of behavior has been of long fascination to scientists and the broader public. It is now widely accepted that most behavioral variation has a genetic component, although the degree of genetic influence differs widely across behaviors. Starting with Mendel's remarkable discovery of "inheritance factors," it has become increasingly clear that specific genetic variants that influence behavior can be identified. This goal is not without its challenges: Unlike pea morphology, most natural behavioral variation has a complex genetic architecture. However, we can now apply powerful genome-wide approaches to connect variation in DNA to variation in behavior as well as analyses of behaviorally related variation in brain gene expression, which together have provided insights into both the genetic mechanisms underlying behavior and the dynamic relationship between genes and behavior, respectively, in a wide range of species and for a diversity of behaviors. Here, we focus on two systems to illustrate both of these approaches: the genetic basis of burrowing in deer mice and transcriptomic analyses of division of labor in honey bees. Finally, we discuss the troubled relationship between the field of behavioral genetics and eugenics, which reminds us that we must be cautious about how we discuss and contextualize the connections between genes and behavior, especially in humans.
Hopi E Hoekstra, Gene E Robinson
Gregor Johann Mendel and the development of modern evolutionary biology
No abstract available
Nils Chr Stenseth, Leif Andersson, Hopi E Hoekstra
Cell atlas of the human ocular anterior segment: Tissue-specific and shared cell types
The anterior segment of the eye consists of the cornea, iris, ciliary body, crystalline lens, and aqueous humor outflow pathways. Together, these tissues are essential for the proper functioning of the eye. Disorders of vision have been ascribed to defects in all of them; some disorders, including glaucoma and cataract, are among the most prevalent causes of blindness in the world. To characterize the cell types that compose these tissues, we generated an anterior segment cell atlas of the human eye using high-throughput single-nucleus RNA sequencing (snRNAseq). We profiled 195,248 nuclei from nondiseased anterior segment tissues of six human donors, identifying >60 cell types. Many of these cell types were discrete, whereas others, especially in the lens and cornea, formed continua corresponding to known developmental transitions that persist in adulthood. Having profiled each tissue separately, we performed an integrated analysis of the entire anterior segment, revealing that some cell types are unique to a single structure, whereas others are shared across tissues. The integrated cell atlas was then used to investigate cell type-specific expression patterns of more than 900 human ocular disease genes identified through either Mendelian inheritance patterns or genome-wide association studies.
Online ahead of print. Vision-dependent and -independent molecular maturation of mouse retinal ganglion cells
The development and connectivity of retinal ganglion cells (RGCs), the retina's sole output neurons, are patterned by activity-independent transcriptional programs and activity-dependent remodeling. To inventory the molecular correlates of these influences, we applied high-throughput single-cell RNA sequencing (scRNA-seq) to mouse RGCs at six embryonic and postnatal ages. We identified temporally regulated modules of genes that correlate with, and likely regulate, multiple phases of RGC development, ranging from differentiation and axon guidance to synaptic recognition and refinement. Some of these genes are expressed broadly while others, including key transcription factors and recognition molecules, are selectively expressed by one or a few of the 45 transcriptomically distinct types defined previously in adult mice. Next, we used these results as a foundation to analyze the transcriptomes of RGCs in mice lacking visual experience due to dark rearing from birth or to mutations that ablate either bipolar or photoreceptor cells. 98.5% of visually deprived (VD) RGCs could be unequivocally assigned to a single RGC type based on their transcriptional profiles, demonstrating that visual activity is dispensable for acquisition and maintenance of RGC type identity. However, visual deprivation significantly reduced the transcriptomic distinctions among RGC types, implying that activity is required for complete RGC maturation or maintenance. Consistent with this notion, transcriptomic alternations in VD RGCs significantly overlapped with gene modules found in developing RGCs. Our results provide a resource for mechanistic analyses of RGC differentiation and maturation, and for investigating the role of activity in these processes.
Irene E Whitney, Salwan Butrus, Michael A Dyer, Fred Rieke, Joshua R Sanes, Karthik Shekhar
Overlapping transcriptional programs promote survival and axonal regeneration of injured retinal ganglion cells
Injured neurons in the adult mammalian central nervous system often die and seldom regenerate axons. To uncover transcriptional pathways that could ameliorate these disappointing responses, we analyzed three interventions that increase survival and regeneration of mouse retinal ganglion cells (RGCs) following optic nerve crush (ONC) injury, albeit not to a clinically useful extent. We assessed gene expression in each of 46 RGC types by single-cell transcriptomics following ONC and treatment. We also compared RGCs that regenerated with those that survived but did not regenerate. Each intervention enhanced survival of most RGC types, but type-independent axon regeneration required manipulation of multiple pathways. Distinct computational methods converged on separate sets of genes selectively expressed by RGCs likely to be dying, surviving, or regenerating. Overexpression of genes associated with the regeneration program enhanced both survival and axon regeneration in vivo, indicating that mechanistic analysis can be used to identify novel therapeutic strategies.
Anne Jacobi, Nicholas M Tran, Wenjun Yan, Inbal Benhar, Feng Tian, Rebecca Schaffer, Zhigang He, Joshua R Sanes
The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics
A key question in theoretical neuroscience is the relation between the connectivity structure and the collective dynamics of a network of neurons. Here we study the connectivity-dynamics relation as reflected in the distribution of eigenvalues of the covariance matrix of the dynamic fluctuations of the neuronal activities, which is closely related to the network dynamics' Principal Component Analysis (PCA) and the associated effective dimensionality. We consider the spontaneous fluctuations around a steady state in a randomly connected recurrent network of stochastic neurons. An exact analytical expression for the covariance eigenvalue distribution in the large-network limit can be obtained using results from random matrices. The distribution has a finitely supported smooth bulk spectrum and exhibits an approximate power-law tail for coupling matrices near the critical edge. We generalize the results to include second-order connectivity motifs and discuss extensions to excitatory-inhibitory networks. The theoretical results are compared with those from finite-size networks and the effects of temporal and spatial sampling are studied. Preliminary application to whole-brain imaging data is presented. Using simple connectivity models, our work provides theoretical predictions for the covariance spectrum, a fundamental property of recurrent neuronal dynamics, that can be compared with experimental data.
Yu Hu, Haim Sompolinsky
Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression
Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.
Jillian Goetz, Zachary F Jessen, Anne Jacobi, Adam Mani, Sam Cooler, Devon Greer, Sabah Kadri, Jeremy Segal, Karthik Shekhar, Joshua R Sanes, Gregory W Schwartz
Brain organoids: the quest to decipher human-specific features of brain development
The development of the human brain occurs largely in utero over long periods of time and is thus experimentally inaccessible; therefore, tractable experimental models are needed. Human brain organoid have emerged as powerful model systems to investigate human-specific features of brain development. Focusing on the cerebral cortex, here, we discuss how brain, and more specifically cortical, organoid models have newly enabled discovery of aspects of progenitor biology and cortical-cell diversification that are unique to humans. We foresee that as advancements in organoid generation increase the complexity of these models, more complete replicas of the brain will empower future studies investigating higher-order aspects of brain biology, toward an understanding of the unique processing capabilities of the human brain.
Ana Uzquiano, Paola Arlotta
A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning
A large body of evidence has indicated that the phasic responses of midbrain dopamine neurons show a remarkable similarity to a type of teaching signal (temporal difference (TD) error) used in machine learning. However, previous studies failed to observe a key prediction of this algorithm: that when an agent associates a cue and a reward that are separated in time, the timing of dopamine signals should gradually move backward in time from the time of the reward to the time of the cue over multiple trials. Here we demonstrate that such a gradual shift occurs both at the level of dopaminergic cellular activity and dopamine release in the ventral striatum in mice. Our results establish a long-sought link between dopaminergic activity and the TD learning algorithm, providing fundamental insights into how the brain associates cues and rewards that are separated in time.
Ryunosuke Amo, Sara Matias, Akihiro Yamanaka, Kenji F Tanaka, Naoshige Uchida, Mitsuko Watabe-Uchida
What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger
When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6- and 7-year-old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call "danger" (how badly one's action could end). In two pre-registered experiments, we tested whether adults and 6- and 7-year-old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences about how much an agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine-grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans.
Nensi N Gjata, Tomer D Ullman, Elizabeth S Spelke, Shari Liu