Kavli Blog

Enhancement of a new DNA-based bioimaging technique… Method allowing near-simultaneous imaging of several thousand neurons in awake behaving mice… Imaging method to overcome biological tissue refractive index inhomogeneity increases large-field-of-view imaging depth… Open-source software package for working with microscopy images boosts high-throughput, single-cell analysis …

Improved DNA conjugation method enhances the achievable labeling density and spatial accuracy of highly multiplexed Exchange-PAINT imaging

Recent developments in high-resolution fluorescence imaging methods have overcome the limits of light diffraction. However, these techniques are challenged by their limited multiplexing capability, which hinders researchers’ understanding of multi-protein interactions at the nanoscale level. Exchange-PAINT (i.e., Points Accumulation in Nanoscale Topography), a new DNA-based approach, boosts multiplexing capabilities by sequentially imaging target molecules using orthogonal, dye-labeled DNA strands. Although very promising for bioimaging, the widespread application of this approach has been limited by the availability of DNA-conjugated ligands for protein labeling. At Harvard University, Dr. Peng Yin and colleagues have developed a new labeling platform for Exchange-PAINT that efficiently conjugates DNA oligonucleotides to various labeling probes (e.g., antibodies, nanobodies, and small molecules). By designing and testing the conjugation of 52 oligonucleotides to labeling probes like nanobodies, the group successfully enhanced the achievable labeling density and spatial accuracy of Exchange-PAINT. Finally, they demonstrated high-resolution cellular imaging with their labeling platform. The DNA conjugation method is simple to perform and the group anticipates that this general framework for labeling protein targets will make Exchange-PAINT accessible to a broader scientific community.

High-resolution image of proteins in HeLa cells acquired using nine rounds of Exchange-PAINT. The target proteins were labeled with DNA-conjugated antibodies using direct immunostaining. Complementary DNA strands were sequentially introduced to the sample for imaging. Post-acquisition, a washing buffer with reduced ionic strength was introduced to remove all DNA strands. Nine imaging rounds were performed using orthogonal DNA strands conjugated to the same dye.

Novel calcium imaging technique with a two-photon light-sculpting system enables fast volumetric imaging across multiple cortical layers

Calcium imaging in mouse hippocampus. (a) Schematic of the window preparation (red box represents imaging volume). (b) Time-averaged image (100μm depth). (c) Calcium traces of individual neurons imaged at 158fps in a single plane. (d) 3D rendering of time-averaged image (0.5 mm × 0.5 mm × 0.2 mm).

Advancing techniques for imaging the mammalian brain requires developing tools that record the activity of all neurons within a functional network at single-neuron resolution and over physiologically relevant time scales. Despite the recent introduction of various high-speed calcium imaging techniques, it remains a challenge to image the functional dynamics of large-scale neuronal circuits in awake-behaving mammals at high resolution. ­­At the Rockefeller University and the University of Vienna, Dr. Alipasha Vaziri and colleagues reveal a new calcium imaging method that utilizes a two-photon light-sculpting system. They updated their previously developed methods by tailoring the microscope to view the typical size of neuronal cell bodies in the mouse cortex. These changes allowed for the samplings of larger volumes with minimal numbers of excitation voxels at near-single-cell resolution. The signal-to-noise ratio was maximized using a fiber-based laser amplifier that synchronized pulses to the imaging voxel speed. The overall approach enabled near-simultaneous calcium imaging of several thousand neurons, across cortical layers (0.5 mm × 0.5 mm × 0.5 mm) and in the hippocampus of awake behaving mice. This exciting new method presents the opportunity to test experimentally a variety of theoretical models of information processing in the mammalian neocortex.

Large-field-of-view imaging by multi-pupil adaptive optics allows position-dependent correction of biological tissue optical distortion

The refractive index inhomogeneity within biological tissue presents challenges for in vivo optical imaging. Adaptive optics (AO) has corrected some of the distor­tions caused by this lack of homogeneity. However, the limited field-of-view (FOV) of current methods reduces imaging speed across larger areas, since distor­tion varies spatially and needs to be corrected accordingly. Approaches that provide simultaneous large-FOV distortion correction, and hence enable imaging of fast dynamics, are needed. At Purdue University, Dr. Meng Cui and colleagues developed multi-pupil adaptive optics (MPAO), which enables simultaneous, position-dependent correction over a 450 × 450 μm2 FOV and expands the correction area to nine times that of previous methods. In conventional AO, the correction measured from one region is applied to the entire image, improving imaging performance within a limited FOV. In MPAO, the imaging procedure is similar to conventional sys­tems, but independent correction for all regions is achieved. By implementing MPAO with imaging of in vivo mouse microglia dynamics, the group demonstrated improved quality compared with conventional AO. They next performed calcium imaging of neurons and astrocytes at 450 μm depth, achieving high-resolution images with full correction. Compared to typical techniques that provide imaging at 200-300 μm depths, large-FOV imaging at ~650 μm depth is possible using MPAO. Finally, with spatially independent distortion control, MPAO also enables nonplanar microscopy (i.e., brings 3D features at different depths into one imaging plane), which the group demonstrated by imaging 3D neurovasculature dynamics in anesthetized mice. This technique can aid high-spatiotemporal-resolution microscopy in various biological systems.

Calcium imaging at 450 μm depth with MPAO. (a,b) Astrocytes at 436-465 μm under the dura with full (a) and system (b) correction. (c,d) Zoomed-in view of the central area in a and b. (e,f) Standard deviation of the time-lapse images of neurons with full (e) and system (f) correction. The images in cf are from the same area. (g) Astrocytes (magenta) and neurons (green) at 450 μm depth. (h) Regions of interest (ROIs) for computing calcium transients. (i) Calcium transients with full and system correction.

A Python platform for image-guided mass spectrometry profiling facilitates sequential multi-technique analysis of each target in a biological sample

Sequential analysis of the same rat cerebellum-derived cell using MS instruments with different capabilities; MALDI-TOF followed by MALDI-FT-ICR.  Once a cell is located in the optical image, its location remains fixed through multiple analyses. MALDI-TOF provides high-throughput screening of thousands of cells to highlight rare or representative individuals. FT-ICR provides exact mass measurement for elemental composition analysis. Such a workflow facilitates exhaustive cell population analysis while efficiently utilizing the FT-ICR instrument.

Image-guided mass spectrometry (MS) profiling is a methodology for analyzing samples ranging from single cells to tissue sections. The workflow uses whole-slide microscopy to select targets, determine their locations, and perform MS analysis at those locations. This framework provides a link between the spatial dimensions in an image and the physical location of a sample. Single-cell MS has attracted substantial interest due to its sensitivity. Biomolecules within cells are detectible with MS, facilitating discovery of single-cell heterogeneity and enhancing understanding of the relationship between cellular chemical contents and their functions. However, limitations in MS imaging for high-throughput, single-cell analysis have stimulated efforts to develop methods that improve efficiency and resolution. At the University of Illinois at Urbana–Champaign, Dr. Jonathan Sweedler and colleagues have developed an open-source software package for working with microscopy images called microMS. The new platform permits effortless sequential analysis, enabling each cell to be scrutinized by multiple techniques. Targets can be automatically located, filtered, and stratified before MS. Specific MS systems are implemented through a novel abstract base class and software architecture, offering impressive simplification of the connection of microMS to new instruments and facilitating more efficientsequential analysis of the same target. The group believes that the ease of extending microMS to a variety of mass spectrometers and other instruments will help advance single-cell profiling.

Intracranial electrical recordings and neuro-stimulation of neurosurgical patients have made fundamental contributions to our understanding of vision, speech, decision making, memory, and sensorimotor processing. The use of these methods has burgeoned over the last decade due to technological advances and an increase in the number of patients undergoing neurosurgery for different neurological disorders. Intracranial electrophysiological research is performed only in patients who are scheduled to undergo neurosurgery, but the combination of treatment with research raises neuroethical issues around informed consent and risk assessment.

The Neuroethics Division of the NIH BRAIN Initiative Multi-Council Working Group (MCWG) serves as a resource of expertise to help navigate ethical considerations associated with cutting-edge science supported under the NIH BRAIN Initiative, such as intracranial electrophysiological research. Dr. Winston Chiong, an assistant Professor in the UCSF Department of Neurology Memory and Aging Center and member of the Neuroethics Division, recently published a paper in Neurosurgery with colleagues Drs. Matthew Leonard and Edward Chang. The group identified ethical dilemmas involved in intracranial electrophysiology research and proposed ethical standards for resolving potential issues.

For instance, patients may be unable to distinguish between clinical treatment and research participation, believing that their treatment is conditional on their participation and therefore making it difficult for them to know when they can refuse participation. Furthermore, the ability of these patients to give consent and understand treatment and research may be impaired by the disorder for which they are being treated, or by psychiatric comorbidities. Participants may also struggle to give informed consent in extra-operative procedures (i.e., testing that continues outside the operating room), as they experience changes in their physical or emotional state or in their medications.

Another ethical theme identified in the paper concerns the dual role of the physician as both clinician and investigator. This dual role may confuse patients about not only the distinction between research and treatment, but also the motivation behind a physician’s recommendations. The authors emphasize the value of strong communication between clinical and research teams and critically, among these teams, patients, and patients’ families. Furthermore, the authors point to the importance of determining how the costs of research will be distinguished from the costs of treatment.

The authors propose modifications to informed consent procedures, including that institutional review boards determine who should obtain consent on a case-by-case basis, and that appropriate methods ensure patient understanding that care is not conditional on participation. They encourage the creation of improved procedures to ensure patients understand the difference between treatment and research and can therefore give informed consent. They state that subject selection should be based on clinical determinations, not research ones. If there is any debate about the proper course of treatment for a subject, they suggest the inclusion of clinicians who are not involved in the research in order to come to a decision. Finally, they encourage the inclusion of bioethics specialists on research teams, to ensure maximized benefits and minimized risks for patients.

As invasive neurosurgical procedures and research become more prevalent, the authors emphasize the importance of adequately addressing these themes, to ensure that we continue making strides in understanding how the brain works while also protecting patients and research participants. Indeed, the NIH BRAIN Initiative emphasizes addressing neuroethical issues associated with neurotechnological advances. On October 26th, the NIH Clinical Center Department of Bioethics in association with the Neuroethics Division of the MCWG of the NIH BRAIN Initiative will host a one-day workshop entitled Ethical Issues in Research with Invasive and Non-Invasive Neural Devices in Humans (details, including a videocast link, will be made available here). These and other efforts can help scientists navigate the novel ethical concerns often raised by breakthrough neuroscience research.

Method to overcome light scatter in optical imaging distinguishes moving objects with high fidelity… Novel analysis method improves accuracy of neural network models… Improved imaging technique reveals synaptic transmission at quantal resolution in fruit fly larvae …

Phase retrieval methods in optical imaging allow for successful imaging of moving targets through scattered media 

A major challenge of optical imaging is the scattering of light, which leads to lower resolution, poorer image quality, and shallower depths of images, especially those involving biological tissue. Researchers have tried to overcome these effects by filtering out repeated scattering of light to measure only the un-scattered or minimally scattered photons. However, such methods fail to address limitations in depth of imaging because they detect only the minimally scattered photons, which are difficult to observe at greater distances. Optical imaging approaches that incorporate information from the scattered photons often require long acquisition times, a reference source (also known as a “guide star”), and dark-field conditions. Recently, Dr. Changhuei Yang and his group at California Institute of Technology have developed a successful method to image moving objects through scattering media. Using temporal and angular correlational analyses from the scattering process, they distinguished objects from their backgrounds, permitting imaging of moving targets that would otherwise have been hidden due to the scattering media. This technique can be used in both dark-field and light-field scenarios, allowing the imaging of non-emitting, transmissive, and reflective non-static samples. This exciting new method could be extended to biological tissue, such as imaging deep brain structures in freely moving animals, and possibly other applications, such as moving objects in dense environments like fog or underwater.

A) Objects were hidden behind a scattering medium and moved 1.5 mm between each acquisition (4 acquisitions per image). B) The raw camera images were blurry and indistinguishable due to the scattering of light from the background. Images remain indistinct based on C) object autocorrelations and D) speckle autocorrelation analyses. E) Using a phase retrieval algorithm, each object was successfully reconstructed.

Functional imaging analysis incorporates connectivity and structural relatedness to improve modeling of brain functional networks

Functional network modeling from resting-state functional magnetic resonance imaging (fMRI) helps clarify how the brain functions normally and could identify biomarkers of neurological and psychiatric disorders. Often, models represent regions of interest (ROIs) in the brain as “nodes” with the functional connectivity between these regions delineated “edges.” At the University of North Carolina, Chapel Hill, Dr. Dinggang Shen’s group developed a new method, connectivity strength-weighted sparse group representation, to model brain functional networks. Like other methods, the team relied on pairwise correlations between ROIs to infer connectivity and made the neurologically reasonable assumption of sparse networks. From BOLD signals, the model assumed that brain regions have ‘first order/direct” interactions with only a few other regions and reduced spurious connections. However, unlike previous methods, the model made use of information on variations in connectivity strength and groupings of similar ROIs. Thus, the team combined pairwise correlations to measure interactions across multiple ROIs, weighted functional connectivity strength to account for network sparsity, and incorporated ROI group structures (a set of regions with similar characteristics) into one model. Using fMRI data from individuals with mild cognitive impairment (MCI), an early indicator of Alzheimer’s disease or another dementia, and from normal controls, the group tested the model on seven performance metrics, including accuracy, sensitivity, and specificity. The team found that their model successfully classified the data as MCI or control better than other brain network connection models with almost 85% accuracy compared to 65%. These results provide validation that this model can identify biomarkers of MCI, which could guide early interventions for Alzheimer’s disease. Shen’s group intends to improve the grouping of related structures in the model further and apply to different brain disorders and diseases.

This figure shows the pattern of nodes and edges that are most likely to indicate MCI classification, an early marker of Alzheimer’s disease. The nodes, represented by green spheres, mark regions of interest and the edges, represented by red and blue lines, identify the connectivity between the regions. These nodes and edges were consistently selected by the new method of connectivity strength-weighted sparse group representation. Note that increased line thickness is represented by increased connectivity as measured by fMRI BOLD response.

Novel quantal resolution imaging technique advances understanding of input-specific plasticity and homeostasis at the Drosophila larval neuromuscular junction

Synapses, including those using glutamate as a neurotransmitter, vary greatly in pre- and post-synaptic transmission properties. How differences in pre-synaptic release characteristics, short term plasticity, and homeostatic stability-promoting regulation relate to one another and to this diversity is poorly understood. At the University of California, Berkeley, Dr. Ehud Isacoff and his group used the Drosophila larval neuromuscular junction (NMJ), a model system for studying glutamatergic transmission, to better understand these mechanisms. They used novel quantal resolution imaging to study the role of input and synapse specificity in the regulation of basal synaptic strength, plasticity, and homeostasis. This technique relies on transgenic Drosophila that express a genetically-encoded calcium indicator, SynapGCaMP6f, allowing for the imaging and quantification of post-synaptic transmission without voltage clamping. Glutamate is released during basal and evoked synaptic events following increases in calcium, revealed by SynapGCaMP6f as changes in fluorescence. Thus, the researchers used fluorescent imaging to examine the probability, frequency, and amplitude of quantal spontaneous, and activity-dependent synaptic transmission of two different synapes onto muscle, those from Ib and Is neurons. By applying this technique with and without motor input, the researchers discovered that Ib and Is synapses have different basal synaptic release characteristics and activity-dependent synaptic modulation at the NMJ. Strikingly, homeostatic compensation in synaptic strength occurred only in the 1b synapses. BRAIN-supported advances in imaging techniques such as this continue to give unprecedented insight into how the nervous system carries out computations and can be expanded across model systems.

Panel A. Baseline fluorescence of the calcium indicator in Ib (blue) relative to Is (orange) at the NMJ. Panel B. Evoked synaptic activity at Ib (blue) relative to Is (orange) at the NMJ. Warmer colors indicate an increased probability of glutamate release. Panels E and F. High-magnification comparison of quantal evoked activity for each neuron.