Reconstruction of burst activity from calcium imaging of neuronal population via Lq minimization and interval screening

High throughput 3D gel-based neural organotypic model for cellular assays using fluorescence biosensors

Three-dimensional (3D) organotypic models that capture native-like physiological features of tissues are being pursued as clinically predictive assays for therapeutics development. A range of these models are being developed to mimic brain morphology, physiology, and pathology of neurological diseases. Biofabrication of 3D gel-based cellular systems is emerging as a versatile technology to produce spatially and cell-type tailored, physiologically complex and native-like tissue models. Here we produce 3D fibrin gel-based functional neural co-culture models with human-iPSC differentiated dopaminergic or glutamatergic neurons and astrocytes. We further introduce genetically encoded fluorescence biosensors and optogenetics activation for real time functional measurements of intracellular calcium and levels of dopamine and glutamate neurotransmitters, in a high-throughput compatible plate format. We use pharmacological perturbations to demonstrate that the drug responses of 3D gel-based neural models are like those expected from in-vivo data, and in some cases, in contrast to those observed in the equivalent 2D neural models.

Fibrin gel-based 3D co-culture models with human-iPSC differentiated dopaminergic or glutamatergic neurons and astrocytes are shown to be functional using biosensors and can be scaled up for high-throughput assays.

Information-Theoretic Approach and Fundamental Limits of Resolving Two Closely Timed Neuronal Spikes in Mouse Brain Calcium Imaging

OBJECTIVE

Although optical imaging of neurons using fluorescent genetically encoded calcium sensors has enabled large-scale in vivo experiments, the sensors' slow dynamics often blur closely timed action potentials into indistinguishable transients. While several previous approaches have been proposed to estimate the timing of individual spikes, they have overlooked the important and practical problem of estimating interspike interval (ISI) for overlapping transients.

METHODS

We use statistical detection theory to find the minimum detectable ISI under different levels of signal-to-noise ratio (SNR), model complexity, and recording speed. We also derive the Cramer-Rao lower bounds (CRBs) for the problem of ISI estimation. We use Monte-Carlo simulations with biologically derived parameters to numerically obtain the minimum detectable ISI and evaluate the performance of our estimators. Furthermore, we apply our detector to distinguish overlapping transients from experimentally obtained calcium imaging data.

RESULTS

Experiments based on simulated and real data across different SNR levels and recording speeds show that our algorithms can accurately distinguish two fluorescence signals with ISI on the order of tens of milliseconds, shorter than the waveform's rise time. Our study shows that the statistically optimal ISI estimators closely approached the CRBs.

CONCLUSION

Our work suggests that full analysis using recording speed, sensor kinetics, SNR, and the sensor's stochastically distributed response to action potentials can accurately resolve ISIs much smaller than the fluorescence waveform's rise time in modern calcium imaging experiments.

SIGNIFICANCE

Such analysis aids not only in future spike detection methods, but also in future experimental design when choosing sensors of neuronal activity.

Fast online deconvolution of calcium imaging data

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(10⁵) traces of whole-brain larval zebrafish imaging data on a laptop.

Calcium imaging methods enable simultaneous measurement of the activity of thousands of neighboring neurons, but come with major caveats: the slow decay of the fluorescence signal compared to the time course of the underlying neural activity, limitations in signal quality, and the large scale of the data all complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are typically applied to imaging data after the experiment is complete. However, in many cases we would prefer to run closed-loop experiments—analyzing data on-the-fly to guide the next experimental steps or to control feedback—and this requires new methods for accurate real-time processing. Here we present a fast activity extraction algorithm addressing both issues. Our approach follows previous work in casting the activity extraction problem as a sparse nonnegative deconvolution problem. To solve this optimization problem, we introduce a new algorithm that is an order of magnitude faster than previous methods, and progresses through the data sequentially from beginning to end, thus enabling, in principle, real-time online estimation of neural activity during the imaging session. This computational advance thus opens the door to new closed-loop experiments.