Visualization of incrementally learned projection trajectories for longitudinal data.

Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer's disease, and its blocking antibody. We uncover valuable insights into the organoids' electrophysiological maturation and response patterns over time under these conditions.

Micro/nano devices for integration with human brain organoids.

Brain organoids are powerful experimental models to study fundamental neurodevelopmental processes and the pathology of neurological disorders. Brain organoids can now be generated from human-induced pluripotent stem cells, which pave the way for using them to investigate effective therapies for various neurodegenerative disorders and diseases. However, brain organoids possess complex cellular architecture, various unknown functionalities, and a lack of vascular networks, which have limited their use in biomedicine and clinical research. Micro/nanoscale devices and technologies can help overcome these limitations. This review critically examines recently developed micro/nano devices for integration with brain organoids. The review focuses on devices designed to achieve several key aims: to improve methodologies for in vitro culture; to enable electrophysiological recordings from organoids; to screen drugs for chemotherapy and new treatments; to understand the effects of psychoactive drugs; and to enable development of vascular networks in organoids. Along with the specific device features and their relevance for these applications, we also discuss the current challenges to overcome and future strategies to advance the use of brain organoids in clinical research. The interdisciplinary convergence of brain organoids research with materials science, device engineering, neuroscience, and stem cell biology holds remarkable potential for replicating the human brain in vitro. Micro/nano devices are an important part of realizing this potential that will afford both fundamental insights into the mechanisms underlying brain function and a pathway for developing novel treatments for neurophysiological and neurodegenerative disorders.

Dimensionality reduction for visualizing high-dimensional biological data.

High throughput technologies used in experimental biological sciences produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high-dimensional data can be aided by human interpretable low-dimensional visualizations. This work investigates how both discrete and continuous structures in biological data can be captured using the recently proposed dimensionality reduction method SONG, and compares the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observe that SONG produces insightful visualizations by preserving various patterns, including discrete clusters, continuums, and branching structures in all considered datasets. More importantly, for datasets containing both discrete and continuous structures, SONG performs better at preserving both the structures compared to UMAP and PHATE. Furthermore, our quantitative evaluation of the three methods using downstream analysis validates the on par quality of the SONG's low-dimensional embeddings compared to the other methods.

Is Viral Vector Gene Delivery More Effective Using Biomaterials?

Gene delivery has been extensively investigated for introducing foreign genetic material into cells to promote expression of therapeutic proteins or to silence relevant genes. This approach can regulate genetic or epigenetic disorders, offering an attractive alternative to pharmacological therapy or invasive protein delivery options. However, the exciting potential of viral gene therapy has yet to be fully realized, with a number of clinical trials failing to deliver optimal therapeutic outcomes. Reasons for this include difficulty in achieving localized delivery, and subsequently lower efficacy at the target site, as well as poor or inconsistent transduction efficiency. Thus, ongoing efforts are focused on improving local viral delivery and enhancing its efficiency. Recently, biomaterials have been exploited as an option for more controlled, targeted and programmable gene delivery. There is a growing body of literature demonstrating the efficacy of biomaterials and their potential advantages over other delivery strategies. This review explores current limitations of gene delivery and the progress of biomaterial-mediated gene delivery. The combination of biomaterials and gene vectors holds the potential to surmount major challenges, including the uncontrolled release of viral vectors with random delivery duration, poorly localized viral delivery with associated off-target effects, limited viral tropism, and immune safety concerns.

Understanding, engineering, and modulating the growth of neural networks: An interdisciplinary approach.

A deeper understanding of the brain and its function remains one of the most significant scientific challenges. It not only is required to find cures for a plethora of brain-related diseases and injuries but also opens up possibilities for achieving technological wonders, such as brain-machine interface and highly energy-efficient computing devices. Central to the brain's function is its basic functioning unit (i.e., the neuron). There has been a tremendous effort to understand the underlying mechanisms of neuronal growth on both biochemical and biophysical levels. In the past decade, this increased understanding has led to the possibility of controlling and modulating neuronal growth in vitro through external chemical and physical methods. We provide a detailed overview of the most fundamental aspects of neuronal growth and discuss how researchers are using interdisciplinary ideas to engineer neuronal networks in vitro. We first discuss the biochemical and biophysical mechanisms of neuronal growth as we stress the fact that the biochemical or biophysical processes during neuronal growth are not independent of each other but, rather, are complementary. Next, we discuss how utilizing these fundamental mechanisms can enable control over neuronal growth for advanced neuroengineering and biomedical applications. At the end of this review, we discuss some of the open questions and our perspectives on the challenges and possibilities related to controlling and engineering the growth of neuronal networks, specifically in relation to the materials, substrates, model systems, modulation techniques, data science, and artificial intelligence.

Sensing refractive index gradients along dielectric nanopillar metasurfaces.

Metasurfaces exhibit unique optical properties that depend on the ratio of their refractive index and that of their surroundings. As such, they are effective for sensing global changes in refractive index based on the shifts of resonances in their reflectivity spectra. However, when used as a biosensor, the metasurface can be exposed to a spatial distribution of biomolecules that brings about gradients in refractive index along the plane of the metasurface. Such gradients produce complex global reflectivity spectrum but with distinct optical enhancements in localized areas along the metasurface. Here, we propose a unique sensing paradigm that images and maps out the optical enhancements that are correlated with the spatial distribution of the refractive index. Moreover, we designed a metasurface whose resonances can be tuned to detect a range of refractive indices. Our metasurface consists of silicon nanopillars with a cylindrical nanotrench at their centers and a metal plane at the base. To assess its feasibility, we performed numerical simulations to show that the design effectively produces the desired reflectivity spectrum with resonances in the near-infrared. Using an incident light tuned to one of its resonances, our simulations further show that the field enhancements are correlated with the spatial mapping of the gradients of refractive indices along the metasurface.

Diamond nanopillar arrays for quantum microscopy of neuronal signals.

Significance: Wide-field measurement of cellular membrane dynamics with high spatiotemporal resolution can facilitate analysis of the computing properties of neuronal circuits. Quantum microscopy using a nitrogen-vacancy (NV) center is a promising technique to achieve this goal. Aim: We propose a proof-of-principle approach to NV-based neuron functional imaging. Approach: This goal is achieved by engineering NV quantum sensors in diamond nanopillar arrays and switching their sensing mode to detect the changes in the electric fields instead of the magnetic fields, which has the potential to greatly improve signal detection. Apart from containing the NV quantum sensors, nanopillars also function as waveguides, delivering the excitation/emission light to improve sensitivity. The nanopillars also improve the amplitude of the neuron electric field sensed by the NV by removing screening charges. When the nanopillar array is used as a cell niche, it acts as a cell scaffolds which makes the pillars function as biomechanical cues that facilitate the growth and formation of neuronal circuits. Based on these growth patterns, numerical modeling of the nanoelectromagnetics between the nanopillar and the neuron was also performed. Results: The growth study showed that nanopillars with a 2 - µ m pitch and a 200-nm diameter show ideal growth patterns for nanopillar sensing. The modeling showed an electric field amplitude as high as ≈ 1.02 × 10 10 mV / m at an NV 100 nm from the membrane, a value almost 10 times the minimum field that the NV can detect. Conclusion: This proof-of-concept study demonstrated unprecedented NV sensing potential for the functional imaging of mammalian neuron signals.

If Human Brain Organoids Are the Answer to Understanding Dementia, What Are the Questions?

Because our beliefs regarding our individuality, autonomy, and personhood are intimately bound up with our brains, there is a public fascination with cerebral organoids, the "mini-brain," the "brain in a dish". At the same time, the ethical issues around organoids are only now being explored. What are the prospects of using human cerebral organoids to better understand, treat, or prevent dementia? Will human organoids represent an improvement on the current, less-than-satisfactory, animal models? When considering these questions, two major issues arise. One is the general challenge associated with using any stem cell-generated preparation for in vitro modelling (challenges amplified when using organoids compared with simpler cell culture systems). The other relates to complexities associated with defining and understanding what we mean by the term "dementia." We discuss 10 puzzles, issues, and stumbling blocks to watch for in the quest to model "dementia in a dish."

Bioinspired surface modification of orthopedic implants for bone tissue engineering.

Biomedical implants have been widely used in various orthopedic treatments, including total hip arthroplasty, joint arthrodesis, fracture fixation, non-union, dental repair, etc. The modern research and development of orthopedic implants have gradually shifted from traditional mechanical support to a bioactive graft in order to endow them with better osteoinduction and osteoconduction. Inspired by structural and mechanical properties of natural bone, this review provides a panorama of current biological surface modifications for facilitating the interaction between medical implants and bone tissue and gives a future outlook for fabricating the next-generation multifunctional and smart implants by systematically biomimicking the physiological processes involved in formation and functioning of bones.

Engineering Highly Interconnected Neuronal Networks on Nanowire Scaffolds.

Identifying the specific role of physical guidance cues in the growth of neurons is crucial for understanding the fundamental biology of brain development and for designing scaffolds for tissue engineering. Here, we investigate the structural significance of nanoscale topographies as physical cues for neurite outgrowth and circuit formation by growing neurons on semiconductor nanowires. We monitored neurite growth using optical and scanning electron microscopy and evaluated the spontaneous neuronal network activity using functional calcium imaging. We show, for the first time, that an isotropic arrangement of indium phosphide (InP) nanowires can serve as physical cues for guiding neurite growth and aid in forming a network with neighboring neurons. Most importantly, we confirm that multiple neurons, with neurites guided by the topography of the InP nanowire scaffolds, exhibit synchronized calcium activity, implying intercellular communications via synaptic connections. Our study imparts new fundamental insights on the role of nanotopographical cues in the formation of functional neuronal circuits in the brain and will therefore advance the development of neuroprosthetic scaffolds.

Efficient multi-site two-photon functional imaging of neuronal circuits.

Two-photon imaging using high-speed multi-channel detectors is a promising approach for optical recording of cellular membrane dynamics at multiple sites. A main bottleneck of this technique is the limited number of photons captured within a short exposure time (~1ms). Here, we implement temporal gating to improve the two-photon fluorescence yield from holographically projected multiple foci whilst maintaining a biologically safe incident average power. We observed up to 6x improvement in the signal-to-noise ratio (SNR) in Fluorescein and cultured hippocampal neurons showing evoked calcium transients. With improved SNR, we could pave the way to achieving multi-site optical recording of fluorogenic probes with response times in the order of ~1ms.

Improved two-photon imaging of living neurons in brain tissue through temporal gating.

We optimize two-photon imaging of living neurons in brain tissue by temporally gating an incident laser to reduce the photon flux while optimizing the maximum fluorescence signal from the acquired images. Temporal gating produces a bunch of ~10 femtosecond pulses and the fluorescence signal is improved by increasing the bunch-pulse energy. Gating is achieved using an acousto-optic modulator with a variable gating frequency determined as integral multiples of the imaging sampling frequency. We hypothesize that reducing the photon flux minimizes the photo-damage to the cells. Our results, however, show that despite producing a high fluorescence signal, cell viability is compromised when the gating and sampling frequencies are equal (or effectively one bunch-pulse per pixel). We found an optimum gating frequency range that maintains the viability of the cells while preserving a pre-set fluorescence signal of the acquired two-photon images. The neurons are imaged while under whole-cell patch, and the cell viability is monitored as a change in the membrane's input resistance.

A polymer optoelectronic interface provides visual cues to a blind retina.

A polymer bulk heterojunction structure utilized as an active photosensitive platform to evoke neuronal activity in a blind retina. The features of the elicited action potentials correlate with the optoelectronic properties of the polymer/electrolyte interface, and resembles the natural response of the retina to light. The polymer interface can be used as an optoelectronic epiretinal interface for retinal prosthesis with no requirement for external power sources or connection cables.

Polymer optoelectronic structures for retinal prosthesis.

This commentary highlights the effectiveness of optoelectronic properties of polymer semiconductors based on recent results emerging from our laboratory, where these materials are explored as artificial receptors for interfacing with the visual systems. Organic semiconductors based polymer layers in contact with physiological media exhibit interesting photophysical features, which mimic certain natural photoreceptors, including those in the retina. The availability of such optoelectronic materials opens up a gateway to utilize these structures as neuronal interfaces for stimulating retinal ganglion cells. In a recently reported work entitled "A polymer optoelectronic interface provides visual cues to a blind retina," we utilized a specific configuration of a polymer semiconductor device structure to elicit neuronal activity in a blind retina upon photoexcitation. The elicited neuronal signals were found to have several features that followed the optoelectronic response of the polymer film. More importantly, the polymer-induced retinal response resembled the natural response of the retina to photoexcitation. These observations open up a promising material alternative for artificial retina applications.

Single-pixel, single-layer polymer device as a tricolor sensor with signals mimicking natural photoreceptors.

Color sensing procedures typically involve multiple active detectors or a photodetector coupled to a filter array. We demonstrate the possibility of using a single polymer layer based device structure for multicolor sensing. The device structure does not require any color filters or any subpixelation, and it distinguishes colors without any external bias. The color sensing relies on an appropriate thickness of the active polymer layer that results in a characteristic polarity and temporal profile of the photocurrent signal in response to various incident colors. The device characteristics reveal interesting similarities to the features observed in natural photosensitive systems including retinal cone cells.