A brain atlas of synapse protein lifetime across the mouse lifespan.

NeuroResource A brain atlas of synapse protein lifetime across the mouse lifespan Graphical abstract Highlights dBrain excitatory synapses differ widely in postsynaptic protein lifetime dSynapses with diverse protein lifetimes are spatiotemporally distributed dAge-resilient synapses have long protein lifetimes dSynapse protein lifetime increases in a model of autism and schizophrenia Authors Edita Bulovaite, Zhen Qiu, Maximilian Kratschke, ..., Anthony Holtmaat, Erik Franse ´n, Seth G.N. Grant Correspondence [email protected] In brief Protein lifetime in synapses is important for brain development and aging, learning, and memory. The authors show that synapses with diverse protein lifetimes are differentially distributed on neurons and in brain regions and that this architecture changes throughout the lifespan and is impacted in a model of autism and schizophrenia. Bulovaite et al., 2022, Neuron 110, 4057–4073 December 21, 2022 ª2022 The Author(s). Published by Elsevier Inc. https://doi.org/10.1016/j.neuron.2022.09.009 ll NeuroResource A brain atlas of synapse protein lifetime across the mouse lifespan Edita Bulovaite, 1 Zhen Qiu, 1 Maximilian Kratschke, 1 Adrianna Zgraj, 1 David G. Fricker, 1 Eleanor J. Tuck, 1 Ragini Gokhale, 1 Babis Koniaris, 1,2 Shekib A. Jami, 3,4 Paula Merino-Serrais, 5,6 Elodie Husi, 7 Lorena Mendive-Tapia, 8 Marc Vendrell, 8 Thomas J. O’Dell, 3,4 Javier DeFelipe, 5,6 Noboru H. Komiyama, 1,9,10,11 Anthony Holtmaat, 7 Erik Franse ´n, 12,13 and Seth G.N. Grant 1,9,14, * 1 Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK 2 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK 3 Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA 4 Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA 5 Laboratorio Cajal de Circuitos Corticales, Centro de Tecnologı ´a Biome ´dica, UPM, 28223 Madrid, Spain 6 Instituto Cajal, CSIC, 28002 Madrid, Spain 7 Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland 8 Centre for Inflammation Research, University of Edinburgh, Edinburgh EH16 4TJ, UK 9 Simons Initiative for the Developing Brain (SIDB), Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9XD, UK 10 The Patrick Wild Centre for Research into Autism, Fragile X Syndrome & Intellectual Disabilities, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9XD, UK 11 Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh EH8 9XD, UK 12 Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden 13 Science for Life Laboratory, KTH Royal Institute of Technology, 171 65 Solna, Sweden 14 Lead contact *Correspondence: [email protected] https://doi.org/10.1016/j.neuron.2022.09.009 SUMMARY The lifetime of proteins in synapses is important for their signaling, maintenance, and remodeling, and for memory duration. We quantified the lifetime of endogenous PSD95, an abundant postsynaptic protein in excitatory synapses, at single-synapse resolution across the mouse brain and lifespan, generating the Protein Lifetime Synaptome Atlas. Excitatory synapses have a wide range of PSD95 lifetimes extending from hours to several months, with distinct spatial distributions in dendrites, neurons, and brain regions. Synapses with short protein lifetimes are enriched in young animals and in brain regions controlling innate behaviors, whereas synapses with long protein lifetimes accumulate during development, are enriched in the cortex and CA1 where memories are stored, and are preferentially preserved in old age. Synapse protein lifetime increases throughout the brain in a mouse model of autism and schizophrenia. Protein lifetime adds a further layer to synapse diversity and enriches prevailing concepts in brain development, aging, and disease. INTRODUCTION The lifetime of a protein at its subcellular location is a fundamental determinant of its role in homeostatic and adaptive processes. Physiological and pathological events trigger protein modifications, and the subsequent replacement with a newly synthesized protein reinstates cellular functions to their original state, a process known as proteostasis (Kaushik and Cuervo, 2015;Labbadia and Morimoto, 2015). In addition to homeostatic roles, protein turnover is essential for adapting the levels of proteins to developmental and adaptive changes in gene expression; for example, a reduction in an mRNA can only lead to reduced levels of protein if there is ongoing protein removal. The speed of the underlying mechanisms that produce, deliver, and remove proteins controls how long it takes for these proteostatic and adaptive processes to complete their task. This in turn is directly related to the lifetime of the protein at its site of action, with a short protein lifetime indicative of rapid homeostasis and adaptation. In brain synapses, protein turnover controls adaptive processes including synaptic transmission, plasticity, and learning (Colledge et al., 2003;Ehlers, 2003;Kato et al., 2005;Patrick et al., 2003). Francis Crick highlighted the importance of synaptic protein lifetime for the duration of memory, reasoning that protein removal erased learning-induced protein modifications (Crick, 1984). An imbalance in the rate of synaptic protein production and removal is thought to underlie learning and other cognitive deficits in genetic forms of autism and intellectual disability (Labbadia and Morimoto, 2015;Louros and Osterweil, Neuron 110, 4057–4073, December 21, 2022 ª2022 The Author(s). Published by Elsevier Inc. 4057 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ll OPEN ACCESS 2016). A reduction in the rate of protein removal is also thought to underlie the synaptic and behavioral deficits in old age (Kaushik and Cuervo, 2015;Sabath et al., 2020;Santra et al., 2019) and contribute to the accumulation of toxic protein species, such as aggregates of b-amyloid and Tau, that contributes to synaptic pathology in Alzheimer’s, Parkinson’s, and other neurodegenerative diseases (Helton et al., 2008;Kaushik and Cuervo, 2015; Tai et al., 2012;Vilchez et al., 2014). Excitatory synapses are highly diverse in molecular composition and morphology (Broadhead et al., 2016;Cizeron et al., 2020;Zhu et al., 2018). The synapse types and subtypes have different spatial distributions on dendrites and between neuron types and are found in characteristic compositions in brain regions and subregions, forming a highly organized synaptome architecture that changes throughout the lifespan (Cizeron et al., 2020). The spatiotemporal differences in synapse molecular composition raise the possibility that synapses may also differ in their protein lifetime. The lifetime of endogenous proteins has been measured in bulk brain synapse preparations, which lack information about synapse diversity (Cohen et al., 2013;Do ¨rrbaum et al., 2018;Ehlers, 2003;El-Husseini et al., 2002;Fornasiero et al., 2018;Heo et al., 2018;Price et al., 2010). Measurements of protein lifetime at single-synapse resolution have been limited to small populations of neurons and exogenously expressed proteins (Gray et al., 2006;Steiner et al., 2008;Villa et al., 2016). To overcome these limitations, we have adapted HaloTag self-labeling protein tag technology to visualize and quantify the lifetime of endogenous synaptic proteins in individual excitatory synapses in all parts of the mouse brain at any age. HaloTag technology (Los et al., 2008) has been widely used to label different proteins in many cellular and tissue contexts, including studies of protein turnover and lifetime. The system involves two components: the genetically encoded HaloTag protein domain and the HaloTag ligand, a small molecule that binds to the HaloTag domain. The ligand forms a highly stable covalent bond that is irreversible under physiological and non-physiological conditions (Encell et al., 2012;England et al., 2015;Hoelzel and Zhang, 2020;Los et al., 2008;Xue et al., 2015). The binding site for a chloroalkane linker is located within a deep pocket (15 A ˚) away from the protein surface, protected from the cellular milieu (Los et al., 2008). Coupling the HaloTag ligand to fluorophores enables irreversible labeling and visualization of tagged proteins, and observing the reduction in fluorescent signal over time enables the lifetime of the protein to be measured. We engineered the mouse genome to express the HaloTag protein domain fused to endogenous PSD95 (PSD95-HaloTag mice) (Figures 1A, S1A, and S1B). PSD95 (also known as DLG4) is a highly abundant scaffold protein of excitatory synapses that interacts with and organizes more than 100 other postsynaptic proteins, including NMDA (N-Methyl-D-aspartic acid) and AMPA (a-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) subtypes of neurotransmitter receptors (Ferna ´ndez et al., 2017,2009;Frank et al., 2016;Husi et al., 2000). PSD95 is essential for synaptic plasticity and learning (Carlisle et al., 2008;Ferna ´ndez et al., 2017;Fitzgerald et al., 2015;Frank et al., 2016;Migaud et al., 1998;Nithianantharajah et al., 2013). Injecting a fluorescent HaloTag ligand into PSD95-HaloTag mice labels excitatory synapses with a fluorescent date-stamp. The lifetime of PSD95 in a synapse is then measured by visualizing the duration of the labeled protein. We combined this labeling strategy with our synaptome mapping pipeline technology (SYNMAP), which is capable of quantifying the fluorescent signals from billions of individual synapses across the mouse brain (Cizeron et al., 2020;Zhu et al., 2018). We report the creation of a single-synapse resolution atlas (Bulovaite et al., 2021a) of PSD95 protein lifetime across the brain in young, mature, and old mice and in a model of neurodevelopmental disorders. RESULTS Genetic labeling and visualization of postsynaptic protein lifetime In PSD95-HaloTag mice, PSD95 expression, protein complex assembly, and the physiology of synaptic transmission andsynaptic plasticity are normal (Figure S1). The bond between PSD95HaloTag and a fluorescent HaloTag ligand (tetramethyl-rhodamine-Halo, or TMR-Halo) proved to be extremely stable (Figure S1G), consistent with previous studies (Encell et al., 2012). To irreversibly label and visualize the PSD95-HaloTag in vivo, we injected a celland blood-brain-barrier-permeable fluorescent ligand (silicon-rhodamine-Halo, or SiR-Halo) (Lukinavi cius et al., 2013;Masch et al., 2018) into the tail vein of PSD95-HaloTag mice and prepared sections of brain tissue for imaging on a spinning disk confocal microscope (pixel resolution 84 nm and optical resolution, as measured by full width at half maximum, 330 nm in x-y and 620 nm in z) (Figure S2). By examining the persistence of labeling after injection, we could identify the synapses and brain regions with different PSD95 lifetimes (Figure 1B). Imaging the brain of 3-month-old PSD95-HaloTag mice 6 h (day 0) after injection revealed widespread labeling (Figures 1C and S3A), whereas none was detected in injected wild-type mice (Figure S3B). 300 nmol of injected SiR-Halo was sufficient to saturate the majority of HaloTag-binding sites (Figure S3C). Higher doses of SiR-Halo were not feasible due to toxic effects of the DMSO solvent (Thackaberry et al., 2014). We confirmed that the SiR-Halo ligand quantitatively labeled all brain regions by injecting it into compound heterozygous Psd95 HaloTag/eGFP mice, quantifying the SiR-Halo and EGFP synaptic puncta density in 110 brain regions, and then testing their correlation (R = 0.976, p < 0.0001) (Figures S3D and S3E). To better understand how long the SiR-Halo ligand remains in the body after injection, we performed a pharmacokinetic analysis (STAR Methods;Figure S4). The peak concentration of SiR-Halo was detected 1 min after injection (blood, 4.6 mM; brain, 0.08 mM), and by 15 min the concentration was 50% of the peak value in both blood and brain. By 24 h, SiR-Halo was undetectable in blood and brain (<10 nM detection threshold). Spatial diversity in synaptic protein lifetime Images collected over the course of 2 weeks revealed a loss of labeling in all brain regions, with different regions showing different rates (Figures 1C and S3A). Synapses with the longest PSD95 protein lifetime were concentrated in the isocortex (neocortex) and hippocampal formation (HPF) (Figures 1C and S3A). Examination of images at single-synapse resolution ll OPEN ACCESS NeuroResource 4058 Neuron 110, 4057–4073, December 21, 2022 C B Spinning disk microscopy PSD95 HaloTag egamIsnoitces niarBdnagil olaH analysis × 100 A ED Motor cortex Stratum radiatum 3 yaD0 yaD41 yaD7 yaD5.1 yaD L2–3 L5 L6a L6b L1 3 yaD0 yaD41 yaD7 yaD5.1 yaD CA1sr CA2sr CA3sr Whole brain yeK41 yaD7 yaD3 yaD5.1 yaD0 yaD HPFIsocortex CA1sr CA1so CA1slm DGmo DGpo SUB CA2 CA3 CB PMY MB TH HY PAL STR OLF CTX HPF L1 L2–3 L4 L5 L6a Puncta • Density • Intensity Synaptome mapping Synapse 1 Short protein lifetime synapse Synapse 2 Long protein lifetime synapse PSD95-HaloTag Unlabelled Labelled SiR-Halo injection Time Figure 1. Visualizing synapse protein lifetime across the mouse brain (A) Experimental flow. Homozygous PSD95-HaloTag mice were injected with a Halo ligand, sagittal brain sections were imaged using spinning disk confocal microscopy, and synaptic puncta were analyzed and mapped. (B) Schematic of PSD95-HaloTag labeling in the postsynaptic terminal of synapses with short or long protein lifetime. (C) SiR-Halo fluorescence labeling at five time points post-injection, showing whole brain, HPF, and layers of the isocortex. Key shows regions and layers (Table S1). (D and E) High-magnification representative images of puncta fluorescence decay in layers of the motor cortex (D) and in stratum radiatum of CA1, CA2, andCA3 subregions of the HPF (E). For brain region/subregion abbreviations, see Table S1. Scale bars: 2 mm (C, whole brain), 500 mm (C, HPF, and CTX), 5 mm (D and E). ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073, December 21, 2022 4059 revealed that each brain region is composed of populations of synapses with different PSD95 lifetimes (Figures 1D, 1E, and S5), some with long (weeks) and others with short (hours or days) protein lifetimes, which we refer to as LPL and SPL synapses, respectively. These synapse populations were spatially organized. For example, within the laminar organization of the isocortex there was a clear gradient from superficial (layer 1) to deep (layer 6) layers, with synapses with the longest PSD95 lifetime located in layer 1 (Figures 1C, 1D, S3A, and S5). In the HPF, the dendritic fields of pyramidal neurons in the CA1 exhibited synapses with longer PSD95 lifetimes than those in CA2 and CA3 (Figures 1C, 1E, S3A, and S5). This pattern was evident in both basal and apical dendrites, indicating that synaptic protein lifetime is determined, at least in part, by cell-wide mechanisms that differ between pyramidal neuron subtypes (Figures 1C and S5). Synapses in the dendritic fields of granule cell neurons in the dentate gyrus (DG) and olfactory bulb had short PSD95 protein lifetimes, further suggesting cell-type-specific turnover mechanisms (Figures 1C and S5). Comparison of the large CA3 thorny excrescence synapses and those in the polymorphic layer of the DG with the smaller CA1 synapses showed that the largest synapses of the HPF (Broadhead et al., 2016;Cizeron et al., 2020;Harris and Weinberg, 2012;Zhu et al., 2018)do not have the longest protein lifetimes (Figure S5). A brainwide synaptome atlas of protein lifetime in excitatory synapses To quantify PSD95 protein lifetime in brain subregions we calculated its half-life using single-synapse resolution data and SYNMAP synaptome mapping technology (Zhu et al., 2018). PSD95-HaloTag mice were injected with SiR-Halo ligand and parasagittal brain sections imaged at post-injection time points (6 h, 1.5, 3, 7, and 14 days) (Figures 1C and S3A). The density and intensity of SiR-Halo-labeled synaptic puncta were quantified in 12 overarching brain areas (isocortex, olfactory areas, HPF, cortical subplate, striatum, pallidum, thalamus, hypothalamus, midbrain, pons, medulla, and cerebellum) and 110 subregions registered to the Allen Reference Atlas (Table S1). By fitting a single-phase exponential decay function to the data for every subregion we calculated the average PSD95 puncta density ( PSD95 density t 1/2 ) and intensity ( PSD95 intensity t 1/2 ) half-lives and plotted the values in brain maps (Figures 2A, 2B, S6A, and S6B). PSD95 density t 1/2 is derived from the reduction in the number of SiR-Halo-positive puncta, whereas PSD95 intensity t 1/2 is derived from the reduction in SiR-Halo signal within labeled synapses. The two half-life measures were highly correlated (R = 0.9088, p < 0.0001; Figure S6C). We also tested if SiR-Halo labeling and puncta detection efficiency in brain subregions influenced protein lifetime measurements and found no correlation (Pearson’s correlation, R = 0.1337, p = 0.1638). The half-life density ( PSD95 density t 1/2 ) and intensity ( PSD95 intensity t 1/2 ) datasets were used to create the Protein Lifetime Synaptome Atlas (Bulovaite et al., 2021a). Dendritic and subregional distribution of synapses with different PSD95 lifetimes We observed a 10-fold difference in PSD95 density t 1/2 between the subregions with the longest (layer 1 of the motor cortex, 12.1 days) and shortest (olfactory bulb glomerular layer, 1.2 days) half-life (Figures 2A, 2C, S6A, and S7A). Comparison of PSD95 density t 1/2 for the 12 overarching brain areas showed that cortical structures are populated by synapses with the longest protein lifetimes, in contrast to subcortical structures which predominantly comprise synapses with the shortest protein lifetimes (Figures 2A, 2C, S6A, S6B, and S7B–S7D). All regions of the isocortex exhibited a gradient, with a 2.4-fold range in PSD95 density t 1/2 from layer 1 (10.6 days) to layer 6 (4.5 days) (Figures 2A, S6A, S7E, and S7F). Similarly, the subregions within the HPF with the shortest PSD95 density t 1/2 were in initial pathways of the trisynaptic circuit (DG, 3.5 days; CA2, 1.9 days; CA3, 3.0 days), whereas those with the longest PSD95 density t 1/2 were in the latter part of the circuit in the CA1 field (5.5 days) and subiculum (6.4 days) (Figures 2B, S7G, and S7H). Although dendrites contain protein synthesis and degradation machinery (Biever et al., 2020;Bingol and Schuman, 2006;Ostroff et al., 2002;Steward and Levy, 1982), it is not known whether synapse protein lifetime varies across the dendritic tree. We quantified PSD95 density t 1/2 in a series of parallel windows from the pyramidal neuron soma in the CA1 to the distal basal dendrites in stratum oriens (so) and to the distal apical dendrites in stratum radiatum (sr), stratum lacunosum (sl), and stratum moleculare (sm) (Figure 2D). In apical dendrites, a gradient of increasing PSD95 protein lifetime was observed (Figure 2D, a–d: linear regression, R 2 = 0.1571, p = 0.007). We next examined segments of the apical dendrite including the sr, sl, and sm, which each receive distinct afferents (from CA3, entorhinal cortex layer 2 and entorhinal cortex layer 3, respectively). A positively sloped gradient in PSD95 lifetime was found in sr (Figure 2D, a and b: linear regression, R 2 = 0.5110, p < 0.0001). There was a significant reduction in PSD95 lifetime between distal sr and the sl (Figure 2D, b and c: Welch’s t test, p = 0.0151), which was not due to differences in synapse size (Figure S8). From the sl to the distal end of the sm we observed a positively sloped gradient (Figure 2D, c and d: linear regression, R 2 = 0.6234, p = 0.0005). PSD95 puncta density followed a similar gradient to that of PSD95 lifetime, but since half-life measure is independent of absolute puncta numbers at day 0, differences in puncta density cannot affect half-life values (Figure S8). In the basal dendrites of so there was an initial gradient in PSD95 protein lifetime before leveling and reversing in distal dendrites. These results show that synapse protein lifetime differs within the dendritic tree of neurons and that synapse protein lifetime is influenced by distance from the soma and by afferent inputs. PSD95 turnover occurs in stable dendritic spines Dendritic spines can be structurally stable for long or short durations (Grutzendler et al., 2002;Holtmaat et al., 2005;Trachtenberg et al., 2002;Zuo et al., 2005). We asked whether protein turnover occurs within stable spines or whether the loss of HaloTag labeling might reflect spine elimination. To visualize dendritic spines housing PSD95 in individual apical dendrites, we dye-filled CA1 pyramidal neurons from PSD95-HaloTag mice injected with SiR-Halo (Figures 3A and S9A). Consistent with previous results that show PSD95 is expressed in a subset of larger, more stable synapses (Cizeron et al., 2020;Fortin et al., 2014;Santuy et al., 2020;Zhu et al., 2018), a subset of spines ll OPEN ACCESS NeuroResource 4060 Neuron 110, 4057–4073, December 21, 2022 expressed PSD95 at day 0, and labeled spines were still visible 10 days after SiR-Halo injection. If PSD95 were being turned over in synapses, we reasoned that we could observe both ‘‘old’’ and ‘‘new’’ protein using a two-step labeling procedure. Three days after injecting mice with SiR-Halo we labeled brain sections with TMR-Halo and found that the two labels were colocalized in many synapses (Figures 3B and S9B). To simultaneously visualize PSD95 turnover and spine turnover in the brain of living PSD95-HaloTag mice, we monitored individual synapses in layer 1 of somatosensory cortex through a 100200406080 Half-life (days) 135791113 Half-life (days) 13579 A BC Isocortex Olfactory areas Hippocampal formation Cortical subplate Axon Dendrite Striatum Pallidum Thalamus Hypothalamus Midbrain Pons Medulla Cerebellum Fiber tracts CA2 CA3 CA1 SUB L3 L2 L2 so sr sl sm DG EC Fraction puncta remaining mutaidaRsneirO Lacunosum MoleculareSoma Distance 00060040020 D 0 0.2 0.4 0.6 0.8 Brain subregion 0 5 10 15 20 Half-life (days) a b c d Figure 2. A brainwide atlas of synapse protein lifetime (A) PSD95 puncta half-life across 110 mouse brain subregions (Table S1). The 12 main brain areas are color-coded to the right. n = 7–9 Psd95 HaloTag/HaloTag animals. (B) PSD95 puncta half-life in HPF, with key elements of the circuitry shown. The pyramidal neurons in entorhinal cortex (EC) layer 2 project to CA1sl, pyramidal neurons in entorhinal cortex layer 3 project to CA1sm, and stellate neurons in entorhinal cortex layer 2 project to DG granule neurons, which in turn project to CA3, which project to CA1 (Marks et al., 2021). (C) Brain subregions ranked according to their half-life (± 95% confidence intervals), color-coded as in (A). (D) Fluorescent puncta decay (mean fraction of puncta remaining at day 7 compared with day 0, ± SD) across apical and basal dendrites of the CA1 pyramidal cells. n = 3 Psd95 HaloTag/HaloTag animals per time point. Reference points for calculating gradients along the apical dendrite are marked by a–d. ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073, December 21, 2022 4061 EGF D SiR-Halo +dendrite dendrite Day 7Day 3 P1 P2 P3 P6 P5 P1 P2 P3 P6 P5 YFP Day 0 5µm P1 P2 P3 P6 P5 P4 Day 0 Day 7 YFP 50 µm 10 µm Vasculature 100 150 50 002468101214 2468101214 00 )syad( emiT)syad( emiT Normalized SiR-Halo puncta intensity (%) 50 100 150 SiR-Halo puncta intensity (AU) P1 P2 P3 P6 P5 P4 SiR-Halo Vessel 1 2–3 5 6a 6b Motor cortex Layer CBA iii iii Day 0 Day 10 Day 0 Day 10 Day 0 Day 10 v vi iv M1 S1 S2 LV Cranial window Skull Dura Dental cement 4 0 x / 0 . 8 Figure 3. PSD95 turnover in dendritic spines (A) 3D reconstructed dye-filled CA1 neurons and SiR-Halo-positive puncta. (i–iv) CA1 apical dendrites (stratum radiatum) intracellularly injected with Alexa 488 (green) at day 0 (i and ii) and at day 10 post-injection of SiR-Halo (red) (iii and iv). (v and vi) Higher-magnification of (i) and (iii), respectively, showing dendritic spines with SiR-Halo-positive puncta (white arrows) and without SiR-Halo signal (blue arrows). Scale bar: 5 mm (i–iv), 1.5 mm (v and vi). (B) Colocalization (orange) of SiR-Halo (red) injected at day 0 and post-fixation-applied TMR-Halo (green) at day 3 in synapses across layers of the motor cortex. Scale bars: 5 mm. (C) Schematic of the cranial window implant above the somatosensory cortex. (D) Bright-field image (left) of the superficial vasculature and, superposed, a fluorescence image of YFP-labeled axons and dendrites in cortical layer 1 that are used as fiducial points for tracking SiR-Halo ligand puncta over time, as shown for day 0 (center) and day 7 (right). (E) A YFP-labeled dendrite (top) containing SiR-Halo-labeled puncta (bottom), repeatedly imaged over days to weeks. SiR-Halo puncta can be tracked in stable and dynamic spines (e.g., P1–P5) and in the immediate surrounding of the dendrite (e.g., P6). (F) The mean (± SD) fluorescence intensities of individual SiR-Halo puncta (n = 295 from 3 mice Psd95 HaloT/HaloTag mice) normalized to the first imaging time point. Red line, a single exponential fit of the means (l= 0.12/day; t 1/2 = 5.8 days). (G) The measured fluorescence intensity over time of the SiR-Halo puncta examples in (E). ll OPEN ACCESS NeuroResource 4062 Neuron 110, 4057–4073, December 21, 2022 Synapse subtypes IsocortexOLFHPF CS STR PAL TH HY MBPCB MY Normalized lifetime PSD95 PSD95 + SAP102 123456789101119202122232425262728293031323334353637 2 34 3 35 5 2010 4 32303736212723 9 24332622 7 19 1 2531282911 8 6 100 80 40 20 0 % remaining A D C B E Type 1: PSD95 only Type 3: PSD95 + SAP102 Synapse type 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 IsocortexOLFHPFCSSTRPAL TH HYMBPCB MY ORB FRP MO SS PTL VIS RSP MOB AON PIR CA1 CA2 CA3 DG SUB 1–6 1–6 1–6 1–6 1–6 1–6 0 2 4 6 8 1012141618 LPL synapses (%) 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35 60 01 6 42213 Time (months) 0.1 1 10 100 0.1 1 10 100 % synapses Type 1 Type 3 s5 s2 s3 s20 s35 s34 (legend on next page) ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073, December 21, 2022 4063 cranial window (Holtmaat et al., 2009) over weeks after injection of SiR-Halo (Figure 3C). Viral vectors expressing YFP were used to visualize dendritic spines and estimate spine size (STAR Methods;Figures 3D and 3E) (Holtmaat et al., 2005). In individual synapses we observed decay profiles that were similar (Figure 3F) to the population data (Figure 2). A comparable overall decay of fluorescence was observed in regions imaged only once over the course of the experiment (Figure S9C), excluding photobleaching as a main factor in driving the fluorescence decay. In line with the dye injection study (Figure 3A), not all spines expressed PSD95 to a similar extent (Figures 3E and 3G), although in general SiR-Halo fluorescence intensities correlated well with spine size (Figure S9C) (Gray et al., 2006). Even within single dendrites, spines variably retained the SiR-Halo label. Label retention was weakly related to spine size (Figure S9C), and spines observed to disappear often had low levels of PSD95 or rapid loss of labeling (Figures 3E and 3G), consistent with published observations (Cane et al., 2014;Gray et al., 2006). Together, these data indicate that structurally stable spines differ in their PSD95 content and lifetime, even within the same dendrite. Synapse classification by protein lifetime The differences in protein lifetime between individual synapses raise questions about the diversity of synapses that comprise the synaptome. In previous work we used a data-driven approach to classify synapses from across the whole brain and lifespan based on their ‘‘static’’ protein composition (Cizeron et al., 2020;Zhu et al., 2018). Using two markers (PSD95eGFP and SAP102mKO2), each of which assembles a set of postsynaptic proteins into distinct molecular complexes (Frank et al., 2016), we classified synapses into three types: type 1 express PSD95, type 2 express SAP102, and type 3 express both markers. Each of these types was further classified using morphological criteria into a total of 37 subtypes (Zhu et al., 2018). To determine whether the different types and subtypes of synapses have characteristic protein lifetimes, we combined synaptome mapping of PSD95-HaloTag with PSD95eGFP and SAP102mKO2 using triple knockin mice (Psd95 HaloTag/eGFP ; Sap102 mKO2/y ). 6-month-old triple knockin mice were injected with SiR-Halo and brain sections collected at day 0 and 7. To test that the estimation of half-life using two time points accurately reflected measurements made with five time points (Figure 2), we correlated the values obtained in all brain subregions and found a very high correlation (puncta density, R = 0.9789, p < 0.0001; puncta intensity, R = 0.9506, p < 0.0001) (Figure S10A). SiR-Halo-positive synapses were categorized using PSD95eGFP and SAP102mKO2, allowing us to determine the PSD95 lifetime of two synapse types (type 1 and type 3) and 30 of the 37 known subtypes (all except subtypes 12–18, which do not express PSD95) in all brain areas. Ranking the synapse subtypes by their PSD95 protein lifetime from brainwide data (Figure 4A) and individual regions (Figure S10B) showed that both type 1 and type 3 synapses contain subtypes with short and long protein lifetimes. Thus, populations of synapses with distinct molecular compositions are further divisible into subtypes with varying protein lifetimes. Synapse subtype 2 maintained the most (96.7%) and subtype 6 the least (23.3%) label after 7 days (Figure 4A). To visualize the potential duration that any synapse subtype could retain copies of PSD95 we plotted exponential decay functions (Figure 4B). This revealed that several synapse subtypes in the adult mouse could retain copies of PSD95 for many months and, potentially, as long as the natural lifespan of the mouse (Burt, 1940;Hamilton, 1937). Building blocks of synaptome architecture The protein lifetime of each synapse subtype was independent of its location in the brain, with subtype lifetime rankings maintained across brain regions (Figures 4C and S10B). This indicates that synapse subtypes with different protein lifetimes are general building blocks of whole-brain architecture. Furthermore, these findings suggest that the spatial distribution of synapse subtypes with different protein lifetimes underpins the halflife measurements from individual brain regions. To confirm this, we generated a self-organizing feature map (SOM), which reduces the high dimensionality of synaptome maps of the 37 subtypes to a two-dimensional representation (Figures 4D and S10C). The SOM shows a systematic organization in which anatomically related regions become closely clustered owing to their similarity in subtype density (Figure S10C). Consistent with our hypothesis, we observe a systematicity in half-lives when subregion half-life values are shown (Figure 4D). LPL synapses are particularly interesting because they could potentially maintain PSD95 modifications for long durations. When grouped together, the five subtypes with longest protein lifetimes (subtypes 2, 34, 3, 35, and 5) represented 6.7% of PSD95-positive excitatory synapses in the whole brain; however, there were marked regional and subregional differences (Figures 4E and S10D), with a 15-fold range across the main brain areas: isocortex (11.9%), striatum (5.9%), cortical subplate (4.4%), cerebellum (3.9%), HPF (3.8%), olfactory areas (3.0%), midbrain (2.4%), hypothalamus (2.2%), medulla (2.2%), pons (2.1%), pallidum (1.8%), and thalamus (0.8%). The subregion with the highest percentage of these LPL synapses was layer 2-3 of the somatosensory cortex (17.2%), while the lowest percentage was found in the CA2 sl-moleculare (0.2%). Isocortex Figure 4. PSD95 protein lifetimes differ between synapse subtypes (A) Ranked bar plot of synapse subtypes (coded 1–37; Zhu et al., 2018) according to their associated PSD95 lifetimes using whole-brain data. 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Neuron 46, 181–189. https://doi.org/10.1016/j.neuron.2005.04.001. ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073, December 21, 2022 4073 STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Mouse monoclonal anti-HaloTag Promega Cat# 9211 RRID: AB_2688011 Mouse anti-PSD95 (clone 16/PSD-95) BD Biosciences Cat# 610496 RRID: AB_397862 Bacterial and virus strains AAV9.CamKII.Cre.SV40 Addgene Cat# 105558 AAV2.Ef1a.DIO.eYFP UNC GTC vector Cat# 42585 Chemicals, peptides, and recombinant proteins 4’,6 diamidino-2-phenylindole (DAPI) Sigma-Aldrich Cat# D9542 Alexa Fluor 488 dye solution Life Technologies Cat# A10436 Pluronic F-127 (20% in DMSO) Thermo Fisher Cat# P3000MP ProLong Gold Antifade mounting medium Life Technologies Cat# P36930 OCT embedding matrix CellPath Cat# KMA-0100-00A TMR-Halo Promega Cat# G8251 SiR-COOH Spirochrome Cat# SC004 Amine (O2) HaloTag Promega Cat# P6711 Deposited data Allen mouse brain reference atlas Allen Brain Institute https://mouse.brain-map.org/static/atlas Synaptome data, including data for time points, individual animals and half-life values This paper https://doi.org/10.7488/ds/3070 Experimental models: Cell lines Embryonic stem cell line used for gene targeting: E14Tg2a 08021401 Experimental models: Organisms/strains Mouse: PSD95 HaloTag/HaloTag This paper N/A Mouse: PSD95 eGFP/eGFP Zhu et al., 2018 N/A Mouse: SAP102 mKO2/mKO2 Zhu et al., 2018 N/A Mouse: Dlg2 -/- McGee et al., 2001 N/A Oligonucleotides Mouse genotyping primers PSD95-HaloTag: exon F, GTCACATGTCTTTGTGACCTTG This paper N/A Mouse genotyping primers PSD95-HaloTag: 95UTR F, GATACATGCAGAGAGGAGTGTC This paper N/A Mouse genotyping primers PSD95-HaloTag: Halogen F, CTGACTGAAGTCGAGATGGAC This paper N/A Recombinant DNA HaloTag coding sequence Promega https://www.promega.co.uk/resources/ technologies/halotag/ Software and algorithms MATLAB MathWorks https://uk.mathworks.com/products/matlab.html ImageJ NIH https://imagej.nih.gov/ij/ Python Python Software Foundation https://www.python.org/ Illustrator Adobe https://www.adobe.com/products/illustrator.html Imaris 7.6.5 Bitplane https://imaris.oxinst.com/ (Continued on next page) ll OPEN ACCESS NeuroResource e1 Neuron 110, 4057–4073.e1–e8, December 21, 2022 RESOURCE AVAILABILITY Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Seth Grant ([email protected]). Materials availability Newly generated materials are available from the lead contact upon request and, where appropriate, provision of a materials transfer agreement. Data and code availability dData are available at the Protein Lifetime Synaptome Atlas website (Bulovaite et al., 2021a) and Edinburgh DataShare (Bulovaite et al., 2021b). dSynaptome map data have been deposited in Edinburgh DataShare and are publicly available as of the date of publication. The DOI is listed in the key resources table. dAll original code has been deposited at GitHub and is publicly available as of the date of publication. DOIs are listed in the key resources table. dAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. EXPERIMENTAL MODEL AND SUBJECT DETAILS Gene targeting and mouse generation Animal procedures were performed in accordance with UK Home Office regulations and approved by Edinburgh University Director of Biological Services. The genetic targeting strategy adapted from Ferna ´ndez et al. (2009) was used to fuse HaloTag protein to the C-terminus of endogenous PSD95. HaloTag coding sequence (Promega) together with a short linker were inserted into the open reading frame of the mouse Psd95 gene, followed by insertion of a loxP floxed PGK-EM7-neo-pA cassette, using recombination in Escherichia coli. E14Tg2a ES cells (from 129P2 ola) were used for gene targeting. Following identification of positive targeting clones using PCR, E3.5 blastocysts from C57BL/6 mice were injected with ES cells containing the target gene. Male chimeras were crossed with C57BL/6 females to produce first generation heterozygous animals. These mice were crossed with CAG Cre recombinase-expressing mice in order to remove the loxP floxed neo cassette in vivo. Animals were then cross-bred to produce a homozygous PSD95-HaloTag colony. PSD95-HaloTag mice were genotyped by PCR using the following primers (5’-3’): exon F, GTCACATGTCTTTGTGACCTTG; 95UTR F, GATACATGCAGAGAGGAGTGTC; and Halogen F, CTGACTGAAGTCGAGATGGAC. Generation and characterization of the Psd95 eGFP/eGFP ;Sap102 mKO2/mKO2 knock-in mouse line was described previously (Zhu et al., 2018). To establish the Psd95 HaloTag/eGFP ;Sap102 mKO2/y line, double homozygous Psd95 eGFP/eGFP ;Sap102 mKO2/mKO2 mice were crossed with Psd95 HaloTag/HaloTag mice. Generation and characterization of the Dlg2 mutant mouse line was described previously (McGee et al., 2001). To establish the Dlg2 +/– ;Psd95 HaloTag/+ line, Dlg2 –/– mice were crossed with Psd95 HaloTag/HaloTag mice. The mice were group housed and littermates were randomly assigned to experimental groups. The following groups of mice (m, male; f, female) were used for the study of PSD95 lifetime: 3 month old animals, day 0 n=9 (5m, 4f), day 1.5 n=8 (4m, 4f), day 3 and day 14 n=8 (3m, 5f), day 7 n=7 (2m, 5f); 3 week old animals, day 0 n=8 (5m, 3f), day 7 n=8 (6m, 2f); 18 month old animals, Continued REAGENT or RESOURCE SOURCE IDENTIFIER ScanImage Vidrio Technologies http://scanimage.vidriotechnologies.com/display/ SIH/ScanImage+Home Correct_3D_Drift (Fiji plugin) Parslow et al., 2014 https://imagej.net/plugins/correct-3d-drift pCLAMP 10.7 Molecular Devices https://support.moleculardevices.com/s/article/ Axon-pCLAMP-10-Electrophysiology-DataAcquisition-Analysis-Software-Download-Page Code for fitting of exponential decay function and Pearson’s correlation test This paper https://github.com/EditaBu/Synapse_Protein_ Lifetime_code_repository.git Code for Bayesian test This paper https://github.com/NilsWinter/matlab-bayesianestimation Other PSD95 protein lifetime maps: resource website This paper http://brain-synaptome.org/Protein_Lifetime/ ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073.e1–e8, December 21, 2022 e2 day 0 n=10 (7m, 3f), day 7 n=9 (8m, 1f). For triple colocalization the groups were: day 0 and day 7, n=8 (8f). For studies in Dlg2 mutant animals, 3to 4-month-old animals were used and the groups were: Dlg2 +/+ ;Psd95 HaloTag/+ animals, day 0 n=5 (2m, 3f), day 7 n=6 (2m, 4f); Dlg2 +/– ;Psd95 HaloTag/+ animals, day 0 n=8 (6m, 2f), day 7 n=8 (3m, 5f); Dlg2 –/– ;Psd95 HaloTag/+ animals, day 0 n=7 (3m, 4f), day 7 n=7 (2m, 5f). Sample size was estimated based on Cizeron et al. (2020). METHOD DETAILS Western blotting PSD95 protein expression and abundance were tested in whole-brain synaptosome extracts from wild-type, Psd95 +/HaloTag and Psd95 HaloTag/HaloTag mice. Three extracts per genotype were loaded onto 4-12% gradient Bis-Tris gels, with 20 ml loaded per well. The gels were subjected to SDS-PAGE and western blotting. The membrane was probed with antibodies for PSD95, PSD95HaloTag and alpha-Tubulin (loading control) and bands detected using the LI-COR Odyssey imaging system. SDS-PAGE analysis Synaptosome preparations from Psd95 +/HaloTag and wild-type mice as well as cell lysates from HEK293 cells expressing cytosolic HaloTag protein alongside wild-type controls were incubated with 1 mM tetramethyl-rhodamine HaloTag ligand (TMR-Halo) for 1 hour at 4 o C. The preparations were then subjected to SDS-PAGE analysis and imaged using the Li-COR Odyssey imaging system. Electrophysiological recordings Hippocampal slices obtained from the dorsal third of the hippocampus were prepared and maintained in vitro using previously described techniques (Babiec et al., 2017) approved by the Institutional Animal Care and Use Committee at the University of California, Los Angeles, USA. In experiments using extracellular recording techniques, slices were maintained at 30 o C in an interface-slice type recording chamber perfused (2-3 ml/min) with an oxygenated (95% O 2 /5% CO 2 ) artificial cerebrospinal fluid (ACSF) containing 124 mM NaCl, 4 mM KCl, 25 mM NaHCO 3 , 1 mM NaH 2 PO 4 , 2 mM CaCl 2 , 1.2 mM MgSO 4 , 10 mM glucose (all Sigma-Aldrich). Field EPSPs (fEPSPs) evoked by Schaffer collateral/commissural fiber stimulation were recorded in stratum radiatum of the CA1 region using low-resistance (5-10 MU) glass recording electrodes filled with ACSF (basal stimulation rate = 0.02 Hz). LTP was induced using two, one-second-long trains of 100 Hz stimulation (inter-train interval = 10 seconds). Whole-cell voltage-clamp recordings of evoked excitatory postsynaptic currents (EPSCs) and miniature EPSCs (mEPSCs) were performed using slices maintained in submergedslice type recording chambers perfused with a modified ACSF containing 2.4 mM KCl, 3.0 mM CaCl 2 , 2.4 mM MgSO 4 , 100 mM picrotoxin. In these experiments, recording electrodes were filled with a solution containing 102 mM Cs-gluconate, 20 mM CsCl, 10 mM K-gluconate, 10 mM TEA-Cl, 5 mM QX-314, 0.2 mM EGTA, 4 mM Mg-ATP, 0.3 mM Na-GTP, 20 mM HEPES (pH 7.3, 290 mOsm). Evoked EPSCs (at 0.2 Hz) were recorded at membrane potentials of -80 mV and +40 mV and AMPAR-mediated and NMDAR-mediated components of the EPSCs were estimated by the amplitude of EPSCs 5 and 50 milliseconds after EPSC onset, respectively. Spontaneous mEPSCs were recorded at -80 mV in the presence of 1.0 mM TTX (Alomone Labs). Data collection and initial analysis were performed blind to genotype. HaloTag ligand generation SiR-Halo coupling and purification were performed as previously described (Lukinavi cius et al., 2013). 50 mg (0.159 mmol) SiRCOOH dye (Spirochrome) and 36 mg (0.161 mmol, 1.5 eq) Amine (O2) HaloTag (Promega) building block were dissolved in 950 ml dimethylformamide (DMF). 90 ml (0.517 mmol, 5 eq) N,N-diisopropylethylamine (DIPEA) were added into the solution. 70 mg (0.135 mmol, 1.3 eq) benzotriazol-1-yl-oxytripyrrolidinophosphonium hexafluorophosphate (PyBOP) dissolved in 230 ml DMF was then added to the SiR-COOH and Amine (O2) HaloTag solution. The solution was stirred at room temperature for 2 hours protected from light. Prior to purification, the compound was phase-separated from DMF. The compound was collected with the organic phase (diethyl ether) while the aqueous phase (brine) was discarded. Two to three tablespoons of dried magnesium sulphate were added to the organic phase solution, mixed well and filtered to remove any aqueous remains. Diethyl ether was evaporated, and the crude product was purified using silica gel column chromatography (2% MeOH in dichloromethane) to give the SiR-Halo ligand (45 mg, 63% yield). In vivo application of HaloTag ligands SiR-Halo ligand was dissolved in DMSO to a stock concentration of 5 mM. HaloTag ligand solution for injections was prepared as described (Grimm et al., 2017) but at 1.5 mM dye concentration and the total injection volume was adjusted by average weight for the animal group examined. 3 week old mice received 70 ml (21 ml HaloTag stock solution, 7 ml Pluronic F-127, 42 ml saline), 3 month old mice received 200 ml (60 ml HaloTag stock solution, 20 ml Pluronic F-127, 120 ml saline) and 18 month old mice received 300 ml (90 ml HaloTag stock solution, 30 ml Pluronic F-127, 180 ml saline) of HaloTag dye solution. Prior to injection, animals were placed in a heat box for 5-10 minutes to allow the blood vessels of the tail to dilate and become more visible. The mice were placed in a rodent restrainer for the injection. A bolus injection of HaloTag ligand solution was performed into the lateral tail vein. Following injection, the animals were monitored for any adverse effects twice daily for the length of the experiment. The experimenter was blinded to time points and genotypes at the time of injections. ll OPEN ACCESS NeuroResource e3 Neuron 110, 4057–4073.e1–e8, December 21, 2022 Tissue collection and section preparation Mice were fully anesthetized by intraperitoneal injection of 0.10-0.20 ml (according to age) pentobarbital (Euthatal). The thorax was opened and an incision made to the right atrium of the heart. A fine needle (26G, 0.45 x 100 mm) was inserted into the left ventricle and the animal was transcardially perfused with 10-15 ml 1xPBS followed by 10-15 ml fixative (4% paraformaldehyde (PFA)) and left at 4 o C for 3-4 hours (according to age). Samples were transferred to 30% sucrose solution and incubated for 48-72 hours at 4 o C. Brains were then embedded in OCT (embedding matrix for frozen sections) solution (CellPath) inside a plastic mould (Sigma-Aldrich), the moulds placed in beakers containing isopentane (Sigma-Aldrich), and the beakers moved to a container containing liquid nitrogen for freezing. Frozen brains were stored at -80 o C for up to 4-5 months. Frozen brain samples were cut at 18 mm thickness using a cryostat (NX70 Thermo Fisher) to obtain sagittal sections referring to 12-13/21 bregma level from the Allen Mouse Brain Atlas (sagittal, https://mouse.brain-map.org/experiment/thumbnails/100042147?image_type=atlas). Cut brain sections were placed on Superfrost Plus glass slides (Thermo Fisher). A drop of 1xPBS was placed on a glass slide prior to picking up the brain sections to ensure the brain tissue lay flat. After cutting, brain sections were left to dry in the dark at room temperature overnight and were then stored at -20 o C. Frozen brain sections were placed in a dark chamber and incubated at room temperature for 1 hour prior to mounting the coverslips. Sections were washed with 800 ml 1xPBS to remove any remaining OCT on or around the brain tissue and allowed to dry. A 12 ml drop of MOWIOL solution was applied on top of the brain section. A glass coverslip (18 mm diameter, thickness #1.5, VWR) was carefully lowered on top of the sample to avoid any bubbles forming in between the glass slide and the coverslip. The sections were left to dry in the dark at room temperature overnight and then stored at 4 o C for up to 1 week. The experimenter was blinded at the stage of tissue sectioning and section preparation for imaging. Post-fixation labeling with HaloTag ligands Brain sections were first washed with 800 ml PBS to remove any remaining OCT and left to dry in the dark. Hydrophobic marker pen was used to draw around sections to contain the solutions. 50 ml10mM TMR-Halo in 1xPBS was added to each brain section and samples incubated for 1 hour at room temperature in a wet dark chamber. Brain sections were then washed twice for 10 minutes each with 1xTBS (Tris buffered saline) containing 0.2% Triton X-100 detergent to remove any unbound TMR-Halo ligand and once with 1xTBS for 10 minutes. Pharmacokinetic analysis Wild-type C57BL/6 mice (n=21, males and females, 2.5-10 months old) were injected with 200 ml 1.5 mM SiR-Halo solution and anaesthetised using isoflurane at different time points post-injection (1, 5, 15, 30 minutes, 1, 6, 24 hours). Additional n=6 mice did not receive an injection of SiR-Halo but were processed as per below and were used as controls. The thorax was opened and a 0.5 ml blood sample was drawn from the left ventricle. The animals were then perfused with 12 ml 1xPBS and decapitated. The brains were dissected and snap frozen in liquid nitrogen. The collected blood samples were left to clot for 20 minutes at room temperature and a further 5 minutes on ice and then centrifuged at 10,000 rpm for 10 minutes. The supernatant was collected, and the resulting serum samples as well as brain samples were stored at -80 o C until further analysis. Each brain sample was homogenized in 7 ml buffer solution containing 1% sodium deoxycolate, 50 mM Tris pH 9 and 100 mM NaCl. Following 20 strokes with a homogenizer, the solution was left on ice for 30 minutes after which another 10 strokes were performed and the solution was incubated for another 30 minutes. The samples were then centrifuged at 50,000 g(20,000 rpm) for 30 minutes at 4 o C. The supernatant was collected, aliquoted and stored at -80 o C. The fluorescence of SiR-Halo in blood serum and brain homogenate samples was detected using BioTek CYTATION 3 spectrophotometer (600 nm excitation wavelength). To increase the fluorescence readout (Lukinavi cius et al, 2013), blood serum samples were diluted in a ratio of 1:2 in 10% SDS solution (in 1xPBS). Brain homogenates were diluted in a ratio of 1:1 in 10% SDS solution (in 1xPBS). Three mice per time point and a triple replicate per sample were performed. CA1 pyramidal neuron filling Mice (n=14) were perfused intracardially with 1xPBS followed by 4% PFA and, after removal, brains were post-fixed in PFA for 24 hours. After washing in PBS, brains were cut in coronal sections (200 mm thick) with a vibratome, and to identify cell nuclei the sections were prelabeled with 4’,6-diamidino-2-phenylindole (DAPI) (Sigma). Then, CA1 pyramidal neurons were individually injected with Alexa Fluor 488 dye solution (Life Technologies) by continuous current until the dendrites were filled completely as previously described (Benavides-Piccione et al., 2013). After injections, sections were mounted in ProLong Gold Antifade mounting medium (Life Technologies). Imaging was performed with a Zeiss LSM 710 confocal microscope. The fluorescence of DAPI, Alexa 488 and 633 (SiR-Halo-positive puncta) was recorded through separate channels. Stacks of images were acquired at high magnification (63x oil immersion; pixel size 0.057 x 0.057 mm; z-step 0.14 mm) and no pixels were saturated within the spines. For intracellular injection experiments, the animals were randomly assigned into groups for different time points for sacrifice. Spinning disk confocal microscopy Imaging was performed using an Andor Revolution XDi spinning disk microscope equipped with CSU-X1 (pinhole size 50 mm) and 2x post-magnification lens. Images of 512 x 512 pixels in size and 16-bit depth were obtained using Andor iXon Ultra back-illuminated EMCCD camera and Olympus UPlanSAPO 100x oil-immersion lens (NA 1.4). To cover the whole area of the sagittal brain section, ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073.e1–e8, December 21, 2022 e4 multi-tile single-plane image acquisition was arranged with no overlap between adjacent tiles. Focus in z-plane was achieved by selecting and recording desired z-position for four points on the brain section, which were then used to calculate the intermediate z-positions for in-between image tiles. The optical resolution was estimated based on reconstructed 3D point spread functions (PSFs) and full width at half maximum (FWHM) measurements on images of near-IR FluoSpheres (diameter: 40 nm, excitation/emission wavelength: 660/680 nm; Invitrogen). 100 ml FluoSpheres solution (1:1000 in 1xPBS) was placed on a glass slide with a brain section from a wild-type mouse and incubated for 30 min. After a wash with 1xPBS, the slides were mounted with #1.5 thickness coverslips using MOWIOL as a mounting medium. Z-stack images were acquired with 150 nm step size and 15-20 image planes were obtained for each bead sample. 3D PSFs from 392 beads were reconstructed to obtain average FWHM values. Imaging parameters High-magnification (x100) images at single-synapse resolution covering the whole brain section were obtained using 250 EM gain, 2-frame averaging and 5000 millisecond acquisition speed. SiR-Halo was excited at 640 nm, TMR-Halo at 561 nm, eGFP at 488 nm and mKO2 at 561 nm. Emitted light was filtered with a QUAD filter (BP 440/40, BP 521/21, BP 607/34 and BP 700/45). Long-term imaging in vivo 5-month-old male (n=3) and female (n=1) homozygous PSD95-HaloTag mice were used in accordance with the guidelines of the Federal Food Safety and Veterinary Office of Switzerland and in agreement with the veterinary office of the Canton of Geneva (license number GE12219A). To generate sparse expression of YFP in the supragranular layers of cortex, a mix of AAV9.CamKII.Cre.SV40 (2.4x10 13 GC/ml; Addgene #105558) and AAV2.Ef1a.DIO.eYFP (4.6x10 12 GC/ml; UNC GTC vector #42585) (100 nl in a ratio 1:1500) was injected in the primary somatosensory cortex. Cranial windows were implanted as described previously (Holtmaat et al., 2009) using a circular (3 mm diameter) coverglass pressed down onto the intact dura and glued to the skull using dental acrylic cement. 2-3 weeks after window implantation, SiR-Halo ligand was injected in the tail vein, as described above. Images were acquired in head-fixed mice under anesthesia (0.1 mg/kg Medetomidin and 5 mg/kg Midazolam, i.p.) using a custom-built, two-photon laser-scanning microscope (2PLSM) (Holtmaat et al., 2009) controlled by custom software written in MATLAB (Scanimage, Vidrio Technologies) (Pologruto et al., 2003). A tunable Ti:sapphire laser (Chameleon ultra II, Coherent) was used as a light source, tuned to l=840 nm for simultaneous excitation of YFP and SiR-Halo ligand, or l=910 nm for YFP alone. Excitation power was kept constant over time. Fluorescent images were collected using a 20x, 0.95 numerical aperture water-immersion objective (Olympus) and GaAsP photomultiplier tubes (10770PB-40, Hamamatsu). Emitted light was spectrally separated using a 565 nm dichroic mirror (Chroma) and two band-pass emission filters (ET510/50 nm, Chroma; 675/67 nm, Semrock). Field-of-views (FOVs) with YFP-labeled dendrites were first imaged at 6 hours post-injection, and then re-imaged after 1, 2, 3, 7, 10 and 14 days. Additional FOVs were imaged only once over the course of the experiment to measure population fluorescence decay similarly to the in situ experiments (Figure S9C). For each FOV, two separate image stacks (typically 10-30 planes, separated by 1 mm) were acquired at 2 milliseconds/line (image size, 1024 x 1024 pixels; pixel size, 0.06 x 0.06 mm), one at excitation l=840 nm and one at l=910 nm. The dim YFP signal that was collected at l=840 nm served to align the dendrites in both image stacks. QUANTIFICATION AND STATISTICAL ANALYSIS Analysis of electrophysiological data Acquisition and analysis of electrophysiological data were performed using pCLAMP software (Molecular Devices). Results are reported as mean ± SEM. Detection of synaptic puncta Synaptic puncta detection from fluorescence images was performed using a machine learning-based ensemble method developed in-house by Dr Zhen Qiu (Cizeron et al., 2020;Zhu et al., 2018). The classifiers/detectors of PSD95eGFP and SAP102mKO2 were trained in a previous study (Zhu et al., 2018). The training of the image detector for PSD95-HaloTag was required. We first collected a training set of 249 images (10.8 310.8 mm) by randomly sampling across 12 main brain regions using bootstrapping. All puncta in the training set were manually located by three independent individuals of varying scientific expertise. Manual annotation of each punctum was then weighted for each individual. The ground truth was generated by weighted averaging over all individuals; an average weight greater than 0.7 was considered as true puncta. The ensemble learning was applied for training and a K-folder method was applied to validate and test the trained detector. Measurement of synaptic parameters Upon detection and localization, synaptic puncta were segmented by applying an intensity threshold. The threshold for each detected punctum was set adaptively at 10% of the height of its fluorescence intensity profile. A set of parameters were then quantified for each segmented punctum, including mean pixel intensity, size, skewness, kurtosis, circularity and aspect ratio (Zhu et al., 2018). Additionally, quantification of puncta density per unit area was performed. For calculations of PSD95 lifetime, primarily the measurements of puncta density and total fluorescence intensity content (punctum size x punctum mean intensity) were used. ll OPEN ACCESS NeuroResource e5 Neuron 110, 4057–4073.e1–e8, December 21, 2022 Comparison of SiR-Halo and eGFP puncta populations Owing to different imaging and puncta detection parameters, fewer SiR-Halo puncta were detected compared with eGFP (Figure S3E). To assess whether SiR-Halo labeled a representative sample of the diverse population of PSD95-positive synapses, total fluorescence intensity distributions for SiR-Halo and eGFP from injected Psd95 eGFP/HaloTag mice were compared. A scaling factor of 0.65 was applied to eGFP puncta distribution to overlay the outlines of the two frequency distributions. The outlines closely overlapped indicating that eGFP and SiR-Halo labeled comparable populations of synaptic puncta, including comparable proportions of puncta of different intensities. These results demonstrate that injection of a fluorescent ligand into PSD95-HaloTag mice efficiently and quantitatively labels PSD95 across all regions of the brain. SiR-Halo-positive puncta 3D reconstruction The z-stacks of SiR-Halo and dye-filled neuron images were analyzed with 3D image processing software Imaris 7.6.5 (Bitplane). SiRHalo-positive puncta were manually reconstructed along apical dendrites (stratum radiatum). For detailed information regarding 3D reconstruction of SiR-Halo-positive puncta, see Figure S9A. Analysis of long-term imaging The images from l=840 nm and l=910 nm acquisitions were overlaid using the Fiji pairwise stitching algorithm (BigStitcher) (Ho ¨rl et al., 2019). The images at different time points were aligned using the ‘Correct_3D_Drift’ plugin in Fiji (Parslow et al., 2014). To correct for slight variations in excitation efficiency and image quality at different time points, the images were normalized using the YFP fluorescence signal from the dendrites, under the assumption that YFP levels did not substantially change over time, and background subtracted using the blood vessel interior as the baseline signal. For single SiR-Halo punctum decay analysis, the total fluorescence of each punctum at every time point was measured from the plane that yielded the brightest signal. For spine SiR-Halo puncta, the spines were selected in the YFP channel. YFP and SiR-Halo fluorescence intensity (from 910 nm and 840 nm excitation, respectively) was integrated in the spine head. To correct for bleaching due to repeated imaging of the puncta, on the first day of imaging a series of seven images were taken of a control FOV in rapid succession (within 5 minutes). For each point in the series the level of bleaching was estimated (3%) and applied as a correction factor to the corresponding time point in the long-term imaging series. Colocalization of synaptic puncta Object-based colocalization method was employed to assess double (between SiR-Halo and eGFP) and triple (between SiR-Halo, eGFP and mKO2) colocalization of markers of synaptic puncta. The double colocalization between two channels (SiR-Halo and eGFP) was obtained by minimizing the summation of the total centroid distance between all eGFP and SiR-Halo using linear programming (Zhu et al., 2018). The triple localization is computationally much more complex and an NP-hard problem, thus it cannot be resolved efficiently using linear programming. New multiple-channel localization algorithms were developed by adapting the multiple hypothesis tracking algorithm (Chenouard et al., 2013) to the particle tracking problem: the image channel of different markers were considered as temporal image frames in tracking, but more image matching between different markers was added because there was no temporal sequence in multilocalization. A distance threshold of 400 nm was applied to identify markers that were colocalized or triple localized. 400 nm is within the range of the previously measured diameters of postsynaptic density as observed by electron microscopy (Harris et al., 1992). Classification of synaptic puncta Using classifiers trained and built in a previous study (Cizeron et al., 2020), Psd95 eGFP/+ ;Sap102 mKO2/y synapses were classified into 37 subtypes based on the parameters measured in the previous steps. A small percentage of puncta (0.002%) that did not get classified into any subtype group were denoted ‘other subtype’. The classification results were then combined with the triple localization result of PSD95-HaloTag to quantify the lifetime of different synapse subtypes over all brain regions/subregions at time points post SiR-Halo injection. Segmentation of brain regions and subregions An overview montage of each imaged sagittal brain section was stitched from 45,000-50,000 image tiles using a custom MATLAB script and downsized by a factor of 16. Quantification of PSD95-Halo labeling was performed on anatomical brain regions as defined by the Allen Mouse Brain Atlas. A combination of manual and semi-automated delineation methods was employed for defining 110 anatomical regions in each sagittal brain image. Manual delineations of subcortical brain regions (subregions of the olfactory areas, cortical subplate, striatum, pallidum, hypothalamus, thalamus, midbrain, cerebellum, pons, medulla) were performed using a polygon selection tool in Fiji/ImageJ (Schindelin et al., 2012;Schneider et al., 2012). Semi-automated delineations of isocortex and hippocampal formation were performed with in-house custom-built software. The program provides a user interface for a freeform deformation of a surface on which we map a specially prepared image that contains a multitude of delineated regions (Sederberg and Parry, 1986). The specially prepared images were, in most cases, delineation templates obtained from the Allen Mouse Brain Atlas. The delineation templates mapped on a digital surface are subdivided into a uniform grid so that each cell contains a subdivision of the delineation template. The software allows the user to manipulate the grid points, which results in the deformation of the grid cells and in turn leads to the deformation of the delineation template (Catmull, 1974). The software superimposes the delineation ll OPEN ACCESS NeuroResource Neuron 110, 4057–4073.e1–e8, December 21, 2022 e6 template on top of the brain image being delineated. The user can deform the delineation template to ‘fit’ the underlying montage image of the brain section. The software has a function allowing the user to paint over any damaged areas on the tissue to exclude them from the finalized delineation. The final delineation is saved as a set of.roi files representing individual brain regions, which are used in the later steps of the mapping pipeline. Decay rate estimation PSD95 decay rate was estimated by two alternative methods: (a) puncta count-based estimation and (b) total punctum intensitybased estimation. Method (a) relies purely on the presence or absence of fluorescent synaptic puncta, whereas method (b) considers the fluorescence content within each punctum. For both methods, the mean at day 0 was considered a reference point to which all the subsequent values were normalized. Single-phase exponential decay function was fitted to fractions of puncta/synapse fluorescence remaining: NðtÞ=N0elt where N(t) denotes the puncta density/fluorescence intensity at time t, N 0 = N(0) is quantity at t = 0 and lis the decay rate constant. Puncta count-based estimation The detected number of fluorescent puncta per 100 mm2 (puncta density) was estimated for each ROI from every brain examined using the SYNMAP pipeline. Normalized puncta densities (y-axis) for each animal were plotted against time (x-axis) and an exponential decay function was fitted to estimate half-life and ± 95% confidence interval. Total puncta intensity-based estimation For each detected punctum within an ROI, mean fluorescence intensity was multiplied by punctum size to obtain the total fluorescence content for each punctum. The resulting fluorescence intensities were summed to obtain the total synaptic fluorescence content within a given brain region. Normalized fluorescence intensities (y-axis) for each animal were plotted against time (x-axis) and an exponential decay function was fitted to estimate half-life and ± 95% confidence interval. PSD95 half-life calculations PSD95 half-life was defined as the time required for the PSD95-Halo puncta density/fluorescence intensity to fall to half its initial value. Half-life was calculated based on the decay constant obtained from exponential curve fitting via the following formula: t1=2=lnð2Þ l PSD95 fraction remaining calculation In order to compare the decay of puncta number/fluorescence intensity between two time points, for each ROI we estimated the fraction of puncta/fluorescence intensity remaining at day 7 compared with day 0. The mean values for fraction remaining were calculated using: f=A B where A represents the mean of day 7 measurements and B is the mean of day 0 measurements. The error in the measurement of fraction remaining was estimated by following the statistical rules for propagation of uncertainty. The formula used for estimating the standard deviation of a ratio of means was: sf=jfjffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sA A2 +sB B2 r where s f is a standard deviation of the function f (ratio of mean (day 7)/mean (day 0)), s A is the standard deviation of A and s B is the standard deviation of B. Statistical analysis Covariance of pairs of synaptic parameters (density, half-life) was assessed using Pearson’s correlation function in MATLAB (Figures S3E and S6C). Correlation coefficient (R) values are presented alongside P-values that determine the statistical significance (P < 0.05) of the correlation between the two variables. Bayesian analysis Changes in subtype densities between 3 month old and 18 month old mice were established in a previous study (Figure S13 in Cizeron et al, 2020) and the heatmaps (Figures 5E and S11C) were then permutated column-wisely based on the ranking order of the subtype lifetime in Figure 4A. The changes in subtype densities (Figures 5E and S11C) were tested using Bayesian estimation (Kruschke, 2013). Bayesian testing was also used to compare PSD95 lifetime between the Dlg2 mouse mutant and control (Figure S12). The input data were modelled assuming a t-distribution, and a Markov chain Monte Carlo algorithm was then performed to estimate the posterior distribution of the changes. P-values were then calculated for the distribution to complete the test. Each data point in each subregion was modelled and tested separately. The results were finally corrected over all subregions using the Benjamini-Hochberg procedure. ll OPEN ACCESS NeuroResource e7 Neuron 110, 4057–4073.e1–e8, December 21, 2022