Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer's disease (AD) and Parkinson's disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders.
We demonstrate label-free three-photon imaging of intact organoids (~2 mm depth) derived from Rett syndrome patients. Long-term imaging of live organoids shows that mutant neurons have shorter migration distances, slower migration speeds and tortuous trajectories.
Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Human cerebral organoids are unique in their development of progenitor-rich zones akin to ventricular zones from which neuronal progenitors differentiate and migrate radially. Analyses of cerebral organoids thus far have been performed in sectioned tissue or in superficial layers due to their high scattering properties. Here, we demonstrate label-free three-photon imaging of whole, uncleared intact organoids (~2 mm depth) to assess early events of early human brain development. Optimizing a custom-made three-photon microscope to image intact cerebral organoids generated from Rett Syndrome patients, we show defects in the ventricular zone volumetric structure of mutant organoids compared to isogenic control organoids. Long-term imaging live organoids reveals that shorter migration distances and slower migration speeds of mutant radially migrating neurons are associated with more tortuous trajectories. Our label-free imaging system constitutes a particularly useful platform for tracking normal and abnormal development in individual organoids, as well as for screening therapeutic molecules via intact organoid imaging. Editor's evaluation This manuscript will be of interest to stem cell and developmental biologists who aim to use newly emerging brain organoid models to understand the structure and function of the developing human brain. It presents a technological advance in imaging and describes an innovative method for labeling and tracking cells within organoids to enable the assessment of dynamic processes within the intact organoid. The method is validated in a disease model and addresses a challenge in the field of human stem cell modeling of assessing cells within the 3D structure. https://doi.org/10.7554/eLife.78079.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Human cerebral organoids derived from embryonic or induced pluripotent stem cells are unique in their ability to recapitulate early events of embryonic brain development (Lancaster et al., 2013). These spheroid structures contain progenitor-rich zones around ventricle-like cavities akin to ventricular zones (VZ) from which neuronal progenitors migrate radially to generate the cortical plate (CP) (Lancaster et al., 2013; Qian et al., 2016; Paşca et al., 2015; Mariani et al., 2012; Kadoshima et al., 2013). Human cerebral organoids also recapitulate gene expression programs of the fetal cortex (Qian et al., 2016; Velasco et al., 2019; Quadrato et al., 2017; Pollen et al., 2019; Luo et al., 2016; Camp et al., 2015) as well as the fetal brain epigenome (Luo et al., 2016). Thus, they have been used to model neurogenesis-relevant human pathologies such as microcephaly (Lancaster et al., 2013; Zhang et al., 2019), licencephaly (Bershteyn et al., 2017), heterotopia (Klaus et al., 2019), Zika virus infection (Qian et al., 2016; Garcez et al., 2016; Dang et al., 2016), and idiopathic autism (Mariani et al., 2015). Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder caused by mutations in the gene encoding Methyl CpG binding protein 2 (MeCP2). MeCP2 is a pleiotropic regulator of gene expression and impacts multiple components of brain development and function (Ip et al., 2018). Structural deficits described in postmortem RTT human brains include reduced cortical thickness, cell size and dendritic arborization (Armstrong et al., 1995; Bauman et al., 1995) and reduced cerebral volume in MR imaging of RTT patients (Carter et al., 2008). These deficits are paralleled by reductions in dendritic arborization, soma size and spine density described in RTT mouse models (Fukuda et al., 2005; Kishi and Macklis, 2004; Shahbazian et al., 2002; Smrt et al., 2007). 2D human stem cell models of RTT, generated by reprogramming of patient cells or genome editing, have revealed deficits in human RTT neurons including aberrant transcription (Lyst and Bird, 2015; Li et al., 2013; Chen et al., 2013; Gomes et al., 2020; Trujillo et al., 2021), impaired neuronal maturation and electrophysiological function (Li et al., 2013; Tang et al., 2016; Kim et al., 2011; Farra et al., 2012), and up- or down-regulation of key signaling pathways and activity-related genes (Li et al., 2013). Using fixed tissue slices from 3D cerebral organoids, we have recently demonstrated impaired proliferation of the progenitor pool and delayed maturation and presumed migration of neurons (Mellios et al., 2011), consistent with human postmortem deficits. However, the use of fixed tissues did not allow cell tracking and analysis of parameters such as speed and trajectory of neuronal displacement; thus the dynamics and cellular mechanisms of the presumptive migration deficit remain to be characterized. Significantly, live cell time-lapse imaging of radial migration of MECP2-deficient neurons from the VZ to CP inside intact live 3D organoids has not been performed so far. The majority of analyses of cerebral organoids have been performed in sectioned tissue (Bershteyn et al., 2017; Klaus et al., 2019; Li et al., 2011; Lancaster and Knoblich, 2014; Andersen et al., 2020; Bagley et al., 2017; Karzbrun et al., 2018; Lancaster et al., 2017; Miura et al., 2020), although recent progress has been made in studying migration using labeled neurons in organoid models (Klaus et al., 2019; Bagley et al., 2017; Birey et al., 2017; Birey et al., 2022; Xiang et al., 2017; Bajaj et al., 2021), including assembloid preparations (Birey et al., 2017; Birey et al., 2022; Xiang et al., 2017). However, labeled live cell imaging generally requires phototoxic dyes and limited incubation conditions, and results as well in labeling mostly peripheral neurons and cells which are not ideal for long-term imaging and for finding ventricular like zones in 3D intact organoids. Even with viral labeling, subtle damage to neuronal integrity can be sometimes detected (Yildirim et al., 2019). Optically, cerebral organoids appear opaque due to the optical density of neuronal tissues and the large number of apoptotic cells in the center of 3D intact conditions (Lancaster and Knoblich, 2014). Thus, label-free and high-resolution deep-tissue imaging are ideally required to perform intact organoid imaging. Third-harmonic generation (THG) is an intrinsic signal which results from tripling of the frequency of the excitation wavelength. THG signal is either generated at structural interfaces such as local transitions of the refractive index or inside the materials whose third order nonlinear susceptibility χ3 is higher. Thus, THG excitation of biological tissues occurs predominantly at interfaces that are formed between aqueous interstitial fluids and lipid-rich structures, such as cellular membrane (Rehberg et al., 2011) and lipid bodies (Débarre et al., 2006). Both standard three-photon fluorescence microscopy and THG microscopy rely on three-photon interaction between ultrashort pulses and tissues. Three-photon fluorescence microscopy and THG microscopy has been recently used to perform structural and functional brain imaging in anesthetized and awake mice (Yildirim et al., 2019; Ouzounov et al., 2017). These studies are based on utilizing a green (GCaMP) genetically engineered calcium indicator (exogenous fluorophores) with their excitation wavelength (1300 nm) which provides peak absorption cross-sections for this indicator. In these three-photon fluorescence microscopy studies, three photons with enough peak power at these excitation wavelengths excite electrons from ground state to excitation states of this indicator. Then, these electrons release their energy while they return to their ground states. During this relaxation, they release emitted photons which have a large 1/e2 bandwidth of fluorescence emission (70 nm for GCaMP6s/f). Therefore, an exogenous label is required to perform three-photon fluorescence structural or functional brain imaging. On the other hand, THG microscopy does not need any label for structural three-photon brain imaging. In addition, three photons with enough peak power at the excitation wavelengths excite electrons from ground state to virtual state so that these electrons do not lose any energy when they come back to the ground state. Therefore, they release emitted photons which have a small 1/e2 bandwidth of emission (~20 nm). Therefore, the emission spectrum of THG imaging occurs at exactly 1/3 of the excitation wavelength. Overall, three-photon fluorescence microscopy is valuable for performing structural and functional brain imaging with fluorescent dyes for which the excitation wavelengths are limited by the peak absorption cross-sections of these fluorophores. However, three-photon fluorescence imaging is prone to phototoxicity and photobleaching particularly with high peak intensity pulses (Yildirim et al., 2019). In contrast, THG microscopy provides label-free structural brain imaging (Yildirim et al., 2020) without problems of phototoxicity and photobleaching - which are its strengths for long-term live cell imaging of cerebral organoids. Optimized designs using high power lasers and high sensitivity photomultipliers have enabled the application of THG microscopy to 3D tissue microscopy by improving the imaging depth in tissues with varying scattering coefficients (Débarre et al., 2006; Yildirim et al., 2015; Yelin and Silberberg, 1999; Gualda et al., 2008). THG microscopy has been applied recently to the non-invasive monitoring of human adipose tissue (Chang et al., 2013), cell nuclei and cytoplasm in liver tissue (Lin et al., 2014) and subcortical structures within an intact mouse brain (Yildirim et al., 2019; Ouzounov et al., 2017; Yildirim et al., 2020). THG imaging provides the advantages of micron scale resolution, label-free imaging, deep tissue penetration depth, and non-destructiveness. Here, we describe a custom-made three-photon microscope with optimized laser and optics design to perform label-free THG imaging of intact organoids, and its use in imaging organoids generated from RTT and control patients to assess early events of early brain development. We demonstrate that high resolution label-free THG signal can be collected from intact cerebral organoids. Comparing organoids from RTT patients and isogenic controls, we show that the ventricular zone in mutant organoids has larger volume, larger surface area, and lower ventricular thickness compared to isogenic control organoids. Finally, label-free live cell imaging provides a unique way of observing neuronal migration inside the VZ/SVZ and CP of developing organoids without any phototoxicitiy or photobleaching, and reveals that the smaller ventricular thickness in mutant organoids is associated with shorter migration distances, more tortuous trajectories, and slower migration speeds of radially migrating neurons. Results Optimized system for label-free THG imaging of intact cerebral organoids We developed a three-photon microscope (Yildirim et al., 2019) and modified it (Figure 1A–C, see Materials and methods) to perform label free THG imaging of intact, uncleared, fixed and live organoids. Since low average laser power is essential for live cell imaging, we focused on optimizing laser and microscopy parameters to reduce the average power requirement for intact organoid imaging. First, we utilized moderate repetition rate (<1 MHz) to maximize the energy per pulse and reduce the average laser power requirement for THG imaging. Second, we minimized the pulse width on the sample by building an external pulse compressor. The pulse width on the sample was reduced to 27 fs in the deepest part of the organoids (see Materials and methods) enabling us to further reduce the average power requirement by two- to threefold compared to other three-photon studies in the literature (Yildirim et al., 2019; Ouzounov et al., 2017; Weisenburger et al., 2019; Figure 1—figure supplements 1 and 2). Finally, we designed all intermediate optics in the excitation and emission path to maximize the generation and collection efficiency of THG signal (Figure 1—figure supplement 3). This optical design procedure enabled us to reduce the spherical aberration in the system and increase the collected signal by twofold compared to off-the shelf optics (Ouzounov et al., 2017; Figure 1—figure supplement 4). Figure 1 with 4 supplements see all Download asset Open asset Three-photon microscope and imaging system. (A) Femtosecond laser pulses from a pump laser (1045 nm) were pumped through a noncollinear optical parametric amplifier (NOPA) to obtain 1300 nm excitation wavelength. Power control of these laser pulses was performed using a combination of a half-wave plate (HWP) and polarizing cube beam splitter (PCBS). A quarter-wave plate (QWP) was used to control the polarization state of the laser pulses to maximize the third harmonic generation (THG) signal. Laser beams were scanned by a pair of galvanometric scanning mirrors (SM), and passed through a scan lens (SL) and a tube lens (TL) on the back aperture of a 1.05 NA, 25×objective. The sample (S) was placed on a two-axis motorized stage, while the objective (OL) was placed on a one-axis motorized stage for nonlinear imaging. Emitted light was collected by a dichroic mirror (CM1), collection optics (CO), laser blocking filters (BF), and nonlinear imaging filters (F) and corresponding collection optics (COA, COB, and COC) for each photomultiplier tube (PMT A, PMT B, and PMT C). (B) Fixed intact organoids were placed inside imaging chamber gaskets on top of the bottom glass. Gaskets were filled with Hprotos and covered with a cover glass. (C) Live organoids were placed on top of the bottom plate of the incubator. Imaging gaskets were placed to secure organoids and cell medium was applied to fill the gaskets. Top plate was placed on top of the gaskets and the microincubator closed with 6 screws. Cell medium mixed with 5% CO2, 5%O2, and balanced N2 was pumped to the chamber; incubator temperature was set to 37 °C. THG signals from intact cerebral organoids reveal distinct zones Cerebral organoids were derived from two pairs of isogenic iPSC clonal lines (see Materials and methods). One pair (Line 1) was produced as previously described (Mellios et al., 2011) from one individual with RTT harboring a heterozygous single nucleotide deletion (frameshift 705delG) in the transcriptional repression domain of MeCP2. Because of the monoallelic expression of X-chromosome genes and clonal selection of iPSCs, the 'RTT-WT' line expressed exclusively the wild-type allele of MECP2, while the "RTT-MT" line expressed exclusively the mutated allele. The second pair of isogenic iPSC clonal lines (Line 2), produced as previously described (Nott et al., 2016), was from another individual with RTT harboring a heterozygous single nucleotide mutation MECP2R306C located in the transcriptional repression domain (TRD) or more specifically NCoR/SMRT interaction domain (NID) of MeCP2, which selectively blocks its interaction with the NCoR/SMRT complex (Lyst and Bird, 2015; Lyst et al., 2013). Because the MECP2R306C-derived iPSCs had skewed X-inactivation, and expressed only the MECP2R306C allele, the MECP2R306C RTT-WT line was generated from the MECP2R306C iPSCs using CRISPR/Cas9-mediated gene editing to correct the R306C mutation (Nott et al., 2016). First, we established that cortex-like substructures could be distinguished in the organoids by cell density differences. In accordance with previous studies (Lancaster et al., 2013; Qian et al., 2016; Velasco et al., 2019), after 35 days of culture, the cerebral organoids demonstrated formation of well-organized structures composed of a ventricular zone (VZ)-like layer (KI67-positive proliferative cells) around a ventricle-like cavity and a surrounding cortical plate (CP)-like structure (Tuj1 or DCX-positive neurons) (Figure 2A–B, see details of the generation of organoids in Materials and methods). Notably, CP and VZ substructures demonstrated distinct cell density and morphology as shown by nuclear (7AAD) stain and plasma membrane specific dye (WGA) (Figure 2C). The VZ region, densely populated by neural progenitor cells and few immature neurons, could be markedly distinguished from the CP substructure with a less dense population of neurons. Thus, we subsequently used the cell density to localize the CP structures in three-photon fluorescence/THG imaging experiments. Figure 2 with 5 supplements see all Download asset Open asset Label-free Third Harmonic Generation (THG) imaging of intact cerebral organoids. (A-B) Confocal imaging of immunolabeled 2D cerebral organoid slices where cerebral organoids present well-organized spherical structures located around ventricle-like cavities. Around these cavities the ventricular zone-like substructure (VZ), densely populated with progenitors (KI67 + cells) and young migrating neurons (TUJ1 + cells), can be distinguished from the cortical plate-like substructure (CP) with a less dense population of neurons (Line 1, TUJ1, DCX + cells). (C) Cortical plate and ventricular zone substructures presented distinct cell density and morphology (Line 2) as shown by nuclear (7AAD dye) and plasma membrane labeling (WGA dye). (D–E). Label-free THG imaging from intact fixed 3D cerebral organoids (Line 2) show distinct signals from the cortical plate and ventricular zone, as confirmed by the nuclear labeling (7AAD dye) observed with the same setting but using three-photon epifluorescence (3PEF). THG signal seems to occur at the plasma membranes in the VZ and to be brighter in the CP substructure. Scale bars are 100 µm. Next, we demonstrated that high-resolution label-free THG signal could be collected from intact cerebral organoids. Nuclear stain was used as a control to localize the high cell density VZ from the low cell density CP (Figure 2D). Strikingly, THG imaging provided a clean delineation between VZ and CP as THG signal intensity was lower in the former area than the latter (Figure 2C–D). The intrinsic THG signal mostly arose distinctly from cell membranes. Consequently, the THG signal nicely revealed the typical radially elongated and bipolar radial glia cell morphology as well as the round-shaped somata of neurons (Figure 2D–E). Additionally, neurons in the CP and newborn neurons inside the VZ showed high intensity somatic signals (Figure 2D–E). Finally, we performed THG imaging of an intact organoid and then stained it with neuronal marker DCX. We were able to find the same field of view in our stained organoids and compare it with our THG imaging results (Figure 2—figure supplement 1). Our results confirmed that strong THG signal is mostly detected in the CP (DCX-rich, low cell density), rather than the VZ (DCX-negative, high cell density) (Figure 2—figure supplement 1). To further validate that the intrinsic THG signal arose from neurons, we expressed green fluorescent protein (GFP) in control organoids by electroporation (see Materials and methods). We previously demonstrated that >90% of the GFP-positive cells in electroporated organoids are neurons (Delepine et al., 2021). Three-dimensional imaging of cells in organoids (~2 mm thickness) revealed that both WT and MT organoids in fixed and live conditions had >85% overlap between GFP and THG signals (Figure 2—figure supplements 2–5, see Methods). Three-dimensional rendering of a~450-image z-stack with 2 µm increments revealed our system's ability to perform high-resolution imaging (Figure 3—videos 1–2; Figure 3A–B, left). We performed depth-resolved imaging of a 250 × 250 × 900 µm3 region selected to include a cortex-like structure, centered around the ventricle-like cavity, for both RTT-WT (Figure 3A) and RTT-MT organoids (Figure 3B). The strong THG signal produced at the interface between an organoid'ssurface and the cover glass helped us determine boundaries of the intact organoids. Since our field of view (FOV) was 250 µm, serial imaging of multiple sites was necessary to delineate individual CP regions. Notably, we were able to collect high-resolution THG signal from the entire 900 µm-deep regions, highlighting the performance of three-photon fluorescence and THG detection for deep-tissue imaging, and in particular, intact uncleared whole organoid imaging. The maximum imaging depth was only limited by the working distance of the objective, which was approximately 2 mm in our experiments (Figure 3—video 3). Figure 3 with 3 supplements see all Download asset Open asset 3-D label-free THG imaging of fixed organoids and characterizing their extinction lengths. (A–B) 3D reconstruction of label-free THG signal from ventricular regions highlighted in Figure 2D–E. THG allows imaging of deep structures (here, till 900 µm). Scale bars represent 100 µm. (C–D) Characterization of the extinction lengths of a WT and a MT fixed organoid at 1300 nm excitation wavelength. Semi-logarithmic plot for ratio of PMT signal and cube of laser power with respect to imaging depth for third harmonic generation (THG) imaging. Slope of these curves result in 162.5 µm and 148.2 µm extinction lengths for the WT and MT fixed organoids, respectively. (E) Comparison of the extinction lengths of six WT and MT fixed organoids (Lines 1 and 2) at 1300 nm excitation wavelength. The average extinction length of wild-type (WT) organoids is significantly higher than that of mutant (MT) organoids (n=6 organoids, p<0.05, t-test, error bars are standard error of the mean [SEM]). Label-free imaging of fixed cerebral organoids reveals differences between RTT and isogenic control organoids First, we characterized the extinction (combined scattering and absorption) lengths of WT and MT organoids. Our label-free THG imaging characterization shows that the extinction lengths of a fixed WT and a MT organoid are 162.5 µm and 148.2 µm, respectively which are approximately half of the extinction length of a primary visual cortex of an awake mouse brain (Yildirim et al., 2019; Yildirim et al., 2020; Figure 3C–D). In other words, fixed WT and MT organoids are at least two times more scattering than the mouse visual cortex at 1300 nm excitation wavelength. We also examined different WT and MT organoids for comparison (Figure 3E). Our results show that average extinction length of WT organoids (152.1±3.5 µm) is significantly higher than that of MT organoids (125.9±4.9 µm, p<0.005, n=6 organoids, t-test). To study the 3D organization of VZ structures in RTT-WT and RTT-MT organoids, depth-resolved serial imaging of whole organoids was performed automatically with a custom algorithm to reduce the total imaging duration (see Materials and methods). We acquired both 7AAD three-photon fluorescence and THG signal from RTT-WT (Figure 4A) and RTT-MT (Figure 4B) organoids. Then, we determined the boundaries of individual VZ structures using THG signal and rendered individual cortical regions in both RTT-WT (Figure 4A) and RTT-MT (Figure 4B) organoids (see Materials and methods, see Figure 4—video 1, see Figure 4—figure supplement 1). First, we quantified the total volume of each region of interest and found slightly (but not significantly) higher volumes in mutant organoids than in control ones (1.13±0.18 × 107 µm3 for RTT-WT, and 1.39±0.15 × 107 µm3 for RTT-MT organoids, p=0.3013; n=10 organoids, t-test; Figure 4C). However, the surface area of the VZ region in RTT-MT organoids was significantly higher than that in RTT-WT organoids (5.70±0.75 × 105 µm2 for RTT-WT, and 11.0±0.71 × 105 µm2 for RTT-MT organoids, p<0.0001; n=10 organoids, t-test; Figure 4C). In addition, the ratio of volume to area was used as a relative measure of the VZ thickness. The progenitor-rich structures in the RTT-MT organoids had significantly lower unit thickness relative to RTT-WT organoids (23.11±4.12 µm for RTT-WT, and 9.99±0.27 µm for RTT-MT organoids, p<0.0001, n=10 organoids, t-test; Figure 4C). Finally, the number of ventricles was significantly higher in RTT-MT organoids (12.3±0.7 for RTT-WT, and 22.0±1.3 for RTT-MT organoids, p<0.0001, n=10 organoids, t-test; Figure 4C). Figure 4 with 6 supplements see all Download asset Open asset Three-dimensional characterization of control (RTT-WT) and mutant (RTT-MT) VZ regions in intact organoids. (A, B). THG signal acquired from the whole RTT-WT (A) and RTT-MT (B) organoids (Line 1) (left) was segmented into individual ventricular regions (right). (C) (left; top) RTT-MT organoids showed slightly higher VZ volume than those in RTT-WT organoids (1.13±0.18 × 107 µm3 for RTT-WT, and 1.39±0.15 × 107 µm3 for RTT-MT organoids, p=0.3013; n=10 organoids, t-test). (left; bottom) The surface area of the VZ region in RTT-MT organoids was significantly higher than that in RTT-WT organoids (5.70±0.75 × 105 µm2 for RTT-WT, and 11.0±0.71 × 105 µm2 for RTT-MT organoids, p<0.0001; n=10 organoids, t-test). (right; top) The VZ thickness (V/A ratio) in RTT-MT organoids was significantly lower than that in RTT-WT organoids (23.11±4.12 µm for RTT-WT, and 9.99±0.27 µm for RTT-MT organoids, p<0.0001, n=10 organoids, t-test). (right; bottom) The number of ventricles was significantly higher in RTT-MT organoids than that in RTT-WT organoids (12.3±0.7 for RTT-WT, and 22.0±1.3 for RTT-MT organoids, p<0.0001, n=10 organoids, t-test). The data are collected from two lines (Line 1 and Line 2, see Figure 4—figure supplements 2–5). Error bars are 90% of the confidence interval (CI). In summary, using label-free intrinsic THG imaging we show that RTT-MT organoids contain an increased number of VZ structures with larger surface area and volume, but lower VZ thickness, compared to RTT-WT organoids (values for each line separately are shown in Figure 4—figure supplements 2–5). Label-free imaging of live cerebral organoids captures lower migration speed and displacement in RTT organoids Direct visualization and time-lapse characterization of neuronal migration dynamics with label-free methods have not been examined so far. Thus, we tracked migrating neurons in real time using THG microscopy. We performed live imaging of RTT-WT and RTT-MT organoids at 35 days in vitro (DIV) for 12–96 hr, with a volumetric acquisition interval of 20 min (Figure 5—figure supplement 1, see Materials and methods). Given the sparseness and high signal to noise ratio, we were able to track cells harboring a bright somatic THG signal in both RTT-WT and RTT-MT organoids (Figure 5A, see Figure 5—video 1Figure 5—videos 1; 2, see Materials and methods). Although the tracked cells in both RTT-WT and RTT-MT organoids migrated radially from the inner VZ to the outer VZ, RTT-MT cells showed decreased displacement, more tortuous trajectories, and reduced linear speed than RTT-WT cells (Figure 5B and C, also see Figure 6). Specifically, we observed in RTT-WT organoids an average migration speed of 23.4±0.2 µm/hr (n=6 organoids, N=208 cells). The average speed for each line was 20.5±0.1 µm/hr for Line 1 and 25.6±0.2 µm/hr for Line 2 (Figure 5—figure supplement 2). These average speed values are comparable to the speed recorded in neurons migrating off a human wild-type cerebral organoid onto a Matrigel-coated surface (Bershteyn et al., 2017) and in ferret cortical explants (Gertz and Kriegstein, 2015). In contrast, RTT-MT cells had a reduced average speed of 13.3±0.1 µm/hour (n=6 organoids, N=217 cells). The average speed for each line was 11.4±0.1 µm/hour for Line 1 and 14.5±0.1 µm/hr for Line 2 (Figure 5—figure supplement 2). Collectively, the average speed in WT organoids was significantly higher than the average speed in MT organoids (Figure 5C). In addition, we compared migration speeds of individual cells with respect to their location in each organoid as well as between organoids (Figure 5—figure supplement 3). In conclusion, we did not find any statistically significant difference in migration speeds of individual cells at different locations in the same organoid (Figure 5—figure supplement 3A-B) as well as between organoids of the same type (Figure 5—figure supplement 3C-D). We also quantified the straightness of trajectory by taking the ratio of displacement and total trajectory length. With a straightness value close to 1, RTT-WT cells (straightness value of 0.881±0.004) had significantly more straight trajectories than RTT-MT cells (0.658±0.006) which showed much more nonlinear migrating patterns (Figure 5A and C). Figure 5 with 9 supplements see all Download asset Open asset Dynamics of neuronal migration in RTT-WT and RTT-MT organoids. (A) Representative time-lapse THG images of migrating cells in the ventricular zone in RTT-WT (top) and RTT-MT (bottom) organoids (Line 1) in every 2 hr. The migrating trajectory of each representative cells is shown on the right panel to the time-lapse images. Color bar represents the instantaneous speed of these cells. (B) Representative track speed over time for cells in RTT-WT (left) and RTT-MT (right) organoids. (C) Summary of displacement, average migration speed and straightness of the migration trajectory (n=6 RTT-WT and 6 RTT-MT organoids, 200 cells for WT and 210 cells for MT. Scale bar is 50 µm. **, p<0.01; ***, p<0.001; ****, p<0.0001, t-test). Error bars are 90% of the confidence interval (CI). Figure 6 Download asset Open asset Dynamics of neuronal migration in RTT-WT and RTT-MT organoids. Representative three-dimensional THG images of the live RTT-WT (A) and RTT-MT (C) organoids from Line 2. Dashed red lines show the planes of imaging presented in (B) and (D). Representative time-lapse, depth-resolved THG images of migr
Noncoding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging. To better understand common genetic variation associated with brain diseases, we defined noncoding regulatory regions for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with variants in transcriptional enhancers and promoters in neurons, sporadic Alzheimer's disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting disease-risk variants in cell-type-specific enhancers to promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia, but not in neurons or astrocytes. These findings revise and expand the list of genes likely to be influenced by noncoding variants in AD and suggest the probable cell types in which they function.
Mutations in MECP2 cause the neurodevelopmental disorder Rett syndrome (RTT). The RTT missense MECP2R306C mutation prevents MeCP2 from interacting with the NCoR/histone deacetylase 3 (HDAC3) complex; however, the neuronal function of HDAC3 is incompletely understood. We found that neuronal deletion of Hdac3 in mice elicited abnormal locomotor coordination, sociability and cognition. Transcriptional and chromatin profiling revealed that HDAC3 positively regulated a subset of genes and was recruited to active gene promoters via MeCP2. HDAC3-associated promoters were enriched for the FOXO transcription factors, and FOXO acetylation was elevated in Hdac3 knockout (KO) and Mecp2 KO neurons. Human RTT-patient-derived MECP2R306C neural progenitor cells had deficits in HDAC3 and FOXO recruitment and gene expression. Gene editing of MECP2R306C cells to generate isogenic controls rescued HDAC3-FOXO-mediated impairments in gene expression. Our data suggest that HDAC3 interaction with MeCP2 positively regulates a subset of neuronal genes through FOXO deacetylation, and disruption of HDAC3 contributes to cognitive and social impairment.
Abstract Background Alzheimer’s disease (AD) is associated with cell type‐specific H3K27ac signatures, an epigenetic modification that marks active enhancers and promoters. Genomic regions marked by H3K27ac, particularly enhancers, are enriched for AD genetic risk. We aim to identify nearby genetic variants regulating histone acetylation to better understand the link between genetic risk and disease. Method For the first time we have performed allelic specific analysis of histone epigenetic modifications in AD. We used H3K27ac ChIP‐seq data from human post‐mortem entorhinal cortex samples from AD cases (n = 24) and controls (n = 23) (Marzi et al., 2018) to differentiate the histone acetylation signal originating from both alleles in an individual. Results We identified 844 significant loci with allele‐specific histone acetylation by testing individual samples, and 344 by testing across samples. Both sets featured an FDR‐significant acetylation imbalance in a region predicted to be regulating SETD1B, a histone lysine methyltransferase previously found to be elevated in AD patients (Cao et al., 2020). Using linear regression, we also identified 329 SNPs associated with histone acetylation levels. Cell type deconvolution of the overlap between the SNP‐associated H3K27ac regions and genomic regions differentially acetylated in AD highlighted multiple brain cell types. In addition, there was an enrichment for biological processes involving myelination, and cell type‐specific regions were enriched for oligodendrocytes. Conclusion Genetic variants are associated with levels of H3K27ac in genomic regions regulating disease‐related processes.
Abstract Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7–40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains ( n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene–trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes.
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