Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,448
datasets available to search
ShareScore release 0.9.0
Dataset results
5,448 results for “Neuron”
Dataset for: Dopamine neurons that inform Drosophila olfactory memory have distinct, acute functions driving attraction and aversion
<p>The brain must guide immediate responses to beneficial and harmful stimuli while simultaneously writing memories for future reference. While both immediate actions and reinforcement learning are instructed by dopamine, how dopaminergic systems maintain coherence between these two reward functions is unknown. Through optogenetic activation experiments, we showed that the dopamine neurons that inform olfactory memory in Drosophila have a distinct, parallel function driving attraction and aversion (valence). Sensory neurons required for olfactory memory were dispensable to dopaminergic valence. A broadly projecting set of dopaminergic cells had valence that was dependent on dopamine, glutamate, and octopamine. Similarly, a more restricted dopaminergic cluster with attractive valence was reliant on dopamine and glutamate; flies avoided opto-inhibition of this narrow subset, indicating the role of this cluster in controlling ongoing behavior. Dopamine valence was distinct from output-neuron opto-valence in locomotor pattern, strength, and polarity. Overall our data suggest that dopamine’s acute effect on valence provides a mechanism by which a dopaminergic system can coherently write memories to influence future responses while guiding immediate attraction and aversion.</p>
Raw image files in Fig. S1E associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains raw ND2 files shown in Fig. S1E associated with: https://doi.org/10.1101/2024.09.24.614704</p>
Raw image files of uncropped western blots associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains uncropped blots for Fig. 1C, S1C, S1D, 2I, and S3B for: https://doi.org/10.1101/2024.09.24.614704</p>
Global ubiquitylation analysis of mitochondria in primary neurons identifies endogenous Parkin targets following activation of PINK1
<p> How activation of PINK1 and Parkin leads to elimination of damaged mitochondria by mitophagy is largely based on cell lines with few studies in neurons. Herein we have undertaken proteomic analysis of mitochondria from mouse neurons to identify ubiquitylated substrates of endogenous Parkin. Comparative analysis with human iNeuron datasets revealed a subset of 49 PINK1 activation-dependent diGLY sites in 22 proteins conserved across mouse and human systems. We employ reconstitution assays to demonstrate direct ubiquitylation by Parkin in vitro. We also identified a subset of cytoplasmic proteins recruited to mitochondria that undergo PINK1 and Parkin independent ubiquitylation indicating the presence of alternate ubiquitin E3 ligase pathways that are activated by mitochondrial depolarisation in neurons. Finally we have developed an online resource to search for ubiquitin sites and enzymes in mitochondria of neurons, MitoNUb. These findings will aid future studies to understand Parkin activation in neuronal subtypes.</p> <p> </p> <p><strong>FILE DECRIPTIONS</strong></p> <p><strong>Figure 1C: Immunoblots for PINK1 signaling in PINK1 WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>Figure1C_GAPDH.tif</strong></li> <li>Parkin shown in <strong>Figure1C_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>Figure1C_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>Figure1C_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>Figure1C_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>Figure1C_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>Figure1C_ S10A_Ubiquitin.tif </strong>(same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure1C_S10A_UbiquitinPSer65.tif </strong>(same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> </ul> <p> </p> <p><strong>Figure 4A: Immunoblots for time-course of Parkin-dependent substrates in C57BL/6J neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure4A_CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_CISD1.tif</strong> (INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure4A_ CPT1</strong><strong>a</strong><strong>.tif</strong> (Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_CPT1</strong><strong>a</strong><strong>.tif</strong> (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure4A_Ubiquitin.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4A_INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure4_UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure4A_INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p><strong>Figure 4B: Immunoblots for validation of Parkin-dependent substrates in PARKIN WT and KO neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure4B_CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li>(bottom blot)<strong> Figure4B_INPUT_CPT1a_CISD1.tif</strong> (INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure4B_ CPT1a.tif</strong> (Halo-multiDSK pull-down) <ul> <li>(top blot)<strong> Figure4B_INPUT_CPT1a_CISD1.tif</strong> ( (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure4B_Ubiquitin.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure4B_INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure4B_UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure4B_INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p> </p> <p><strong>Figure 5A: Immunoblots for <em>in vitro</em> reconstitution assay in PINK1 WT and KO mouse embryonic fibroblasts.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1shown in <strong>Figure5A_CISD1.tif</strong></li> <li>CPT1α shown in <strong>Figure5A_ CPT1a.tif</strong></li> <li>CYB5B shown in <strong>Figure5A_CYB5B.tif</strong></li> <li>HK1 shown in <strong>Figure5A_HK1.tif</strong></li> <li>MFN2 shown in <strong>Figure5A_MFN2.tif</strong></li> <li>VDAC shown in <strong>Figure5A_VDAC.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>Figure5A_ParkinPSer65.tif</strong></li> <li>Parkin shown in <strong>Figure5A_Parkin.tif</strong></li> <li>Ubiquitin shown in <strong>Figure5A_Ubiquitin.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure5A_UbiquitinPSer65.tif </strong></li> </ul> <p> </p> <p><strong>Figure 5B: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>CPT1α shown in <strong>Figure5B_ CPT1 </strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>Flag-Ub (CPT1α) shown in <strong>Figure5B_ CPT1 </strong><strong>α_Flag_800nm</strong><strong>.tif</strong></li> <li>His (CPT1α) shown in <strong>Figure5B_ CPT1 </strong><strong>α_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <ul> <li>Miro1 shown in <strong>Figure5B_Miro1_800nm.tif</strong></li> <li>Flag-Ub (Miro1) shown in <strong>Figure5B_ Miro1</strong><strong>_Flag_800nm</strong><strong>.tif</strong></li> <li>His (Miro1) shown in <strong>Figure5B_ Miro1</strong><strong>_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <p><strong>Figure 5C: Immunoblots for time-course of CPT1</strong><strong>α</strong><strong> ubiquitylation in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>CPT1α shown in top blot: <strong>Figure5C_Flag_CPT1</strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>Flag-Ub shown in bottom blot: <strong>Figure5C_Flag_CPT1</strong><strong>α_800nm</strong><strong>.tif</strong></li> <li>His shown in <strong>Figure5C_ CPT1 </strong><strong>α_His_800nm</strong><strong>.tif</strong></li> </ul> <p> </p> <p><strong>Figure S3A: Immunoblots for PINK1 signaling in C57BL/6J neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS3A_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS3A_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS3A_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS3A_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>FigureS3A_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS3A_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS3A_Ubiquitin.tif </strong>(same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS3A_UbiquitinPSer65.tif </strong>(same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser111 Rab8A and Phospho-Ser65 Parkin also shown in <strong>FigureS3A_ParkinPSer65_Rab8APSer111.tif</strong></li> </ul> <p> </p> <p><strong>Figure S3B: Immunoblots for comparison of Halo-multiDSK and Halo-TUBE in C57BL/6J neurons.</strong></p> <ul> <li>Ubiquitin shown in <strong>FigureS9_S3_Ubiquitin.tif </strong>(same blot used for Figure_S9)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS9_S3_UbiquitinPSer65.tif </strong>(same blot used for Figure_S9)</li> </ul> <p> </p> <p><strong>Figure S3C: Immunoblots for PINK1 signaling in Parkin WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS3C_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS3C_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS3C_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS3C_PINK1.tif </strong>(immunoprecipitation)</li> <li>Rab8A shown in <strong>FigureS3C_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS3C_Rab8APSer111.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS3C_ Ubiquitin.tif </strong>(same blot used for Figure_4B, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS3C_ UbiquitinPSer65.tif </strong>(same blot used for Figure_4B, Halo-multiDSK pull-down)</li> </ul> <p> </p> <p><strong>Figure S4: Immunoblots for PINK-Parkin signaling in VPS35 D620N mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS4_GAPDH.tif</strong></li> <li>Parkin shown in <strong>FigureS4_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS4_ParkinPSer65.tif</strong></li> <li>Rab8A shown in <strong>FigureS4_Rab8A.tif</strong></li> <li>Phospho-Ser111 Rab8A shown in <strong>FigureS4_Rab8APSer111.tif</strong></li> <li>CISD1 shown in <strong>FigureS4_ CISD1.tif </strong>(Halo-UBQLN1 pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS4_ UbiquitinPSer65.tif</strong> (Halo-UBQLN1 pull-down)</li> <li>VPS35 shown in <strong>FigureS4_VPS35.tif</strong></li> </ul> <p>Scan of Memcode shown in <strong>FigureS4_Memcode.tif</strong></p> <p> </p> <p><strong>Figure S6: Immunoblots for biochemical analysis in C56BL/6J mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS6_GAPDH.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS6_ UbiquitinPSer65.tif </strong></li> </ul> <p> </p> <p><strong>Figure S8: Immunoblots for biochemical analysis in PINK1 WT and KO mouse cortical neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>GAPDH shown in <strong>FigureS8_GAPDH.tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS8_ UbiquitinPSer65.tif</strong></li> </ul> <p> </p> <p><strong>Figure S9: Immunoblots for biochemical analysis of ubiquitylated target in C56BL/6J mouse cortical neurons.</strong></p> <p>Membrane-enriched lysate subjected to ubiquitin capture using UBQLN1(TUBE), multiDSK and mutant multiDSK pull-down (Illustration of sample loading in figures FigureS9_ACSL6,_MFN2, _UbiquitinPSer65, _NAV1.7).</p> <p>Scans of X-ray film:</p> <ul> <li>Ubiquitin shown in <strong>FigureS9_S3_Ubiquitin.tif </strong>(same blot used for Figure_S3B)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS9_S3_UbiquitinPSer65.tif </strong>(same blot used for Figure_S3B)</li> <li>ABCD3 shown in <strong>FigureS9_ABCD3.tif</strong></li> <li>ACSL1shown in <strong>FigureS9_ ACSL1.tif</strong></li> <li>ACSL6 shown in <strong>FigureS9_ACSL6.tif</strong></li> <li>AGPAT5 shown in <strong>FigureS9_AGPAT5.tif</strong></li> <li>ARHGAP33 shown in <strong>FigureS9_ARHGAP33.tif</strong></li> <li>ATAD1 shown in <strong>FigureS9_ATAD1.tif</strong></li> <li>CAD shown in <strong>FigureS9_ CAD.tif</strong></li> <li>CAMK2A shown in <strong>FigureS9_CAMK2A.tif</strong></li> <li>CAMK2B shown in <strong>FigureS9_CAMK2B.tif</strong></li> <li>CDK16 shown in <strong>FigureS9_CDK16.tif</strong></li> <li>CISD1shown in <strong>FigureS9_CISD1.tif</strong></li> <li>CNN3 shown in<strong> FigureS9_CNN3.tif</strong></li> <li>CPT1α shown in <strong>FigureS9_ CPT1A.tif</strong></li> <li>CYB5B shown in <strong>FigureS9_CYB5B.tif</strong></li> <li>CYB5R3 shown in <strong>FigureS9_CYB5R3.tif</strong></li> <li>DCAKD shown in <strong>FigureS9_DCAKD.tif</strong></li> <li>DCAMKL2 shown in <strong>FigureS9_DCAMKL2.tif</strong></li> <li>FAM213A shown in <strong>FigureS9_FAM213A.tif</strong></li> <li>FBXO41 shown in <strong>FigureS9_FBXO41.tif</strong></li> <li>GK shown in <strong>FigureS9_GK.tif</strong></li> <li>HK1 shown in <strong>FigureS9_HK1.tif</strong></li> <li>HSDL1 shown in <strong>FigureS9_HSDL1.tif</strong></li> <li>MAO-A shown in <strong>FigureS9_MAOA.tif</strong></li> <li>MAO-B shown in <strong>FigureS9_MAOB.tif</strong></li> <li>MAPRE2 shown in <strong>FigureS9_MAPRE2.tif</strong></li> <li>MARC2 shown in <strong>FigureS9_MARC2.tif</strong></li> <li>MFN1 shown in <strong>FigureS9_MFN1.tif</strong></li> <li>MFN2 shown in <strong>FigureS9_MFN2.tif</strong></li> <li>NAV1.7 shown in <strong>FigureS9_NAV1.7.tif</strong></li> <li>P23 shown in <strong>FigureS9_p23.tif</strong></li> <li>PRKCG shown in <strong>FigureS9_PRKCG.tif</strong></li> <li>RAB5C shown in <strong>FigureS9_Rab5c.tif</strong></li> <li>RHOT2 shown in <strong>FigureS9_RHOT2.tif</strong></li> <li>RIMS4 shown in <strong>FigureS9_RIMS4.tif</strong></li> <li>RUFY3 shown in <strong>FigureS9_RUFY3.tif</strong></li> <li>SH3BP4 shown in <strong>FigureS9_SH3BP4.tif</strong></li> <li>SNX3 shown in <strong>FigureS9_SNX3.tif</strong></li> <li>TDRKH shown in <strong>FigureS9_TDRKH.tif</strong></li> <li>TOMM70 shown in <strong>FigureS9_TOMM70.tif</strong></li> </ul> <p> </p> <p><strong>Figure S10A: Immunoblots for validation of Parkin-dependent substrates in PINK1 WT and KO neurons.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure S10A _CISD1.tif </strong>(Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_CISD1.tif </strong>(INPUT)</li> </ul> </li> <li>CPT1α shown in <strong>Figure S10A _ CPT1A.tif</strong> (Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_ CPT1A.tif</strong> (INPUT)</li> </ul> </li> <li>Ubiquitin shown in <strong>Figure1C_ S10A _Ubiquitin.tif </strong>(same blot used for FigureS1C, Halo-multiDSK pull-down) <ul> <li><strong> Figure S10A _INPUT_Ubiquitin.tif</strong> (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure1C_ S10A_UbiquitinPSer65.tif </strong>(same blot used for FigureS1C, Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure S10A _INPUT_GAPDH.tif </strong>(INPUT)</li> </ul> <p> </p> <p><strong>Figure S10B: Immunoblots for time-course of Parkin-dependent substrates in SH-SY5Y cells.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1 shown in <strong>Figure S10B _CISD1.tif </strong>(Halo-multiDSK pull-down)</li> <li>CPT1α shown in <strong>Figure S10B _ CPT1A.tif</strong> (Halo-multiDSK pull-down)</li> <li>Ubiquitin shown in <strong>Figure S10B _Ubiquitin.tif </strong>(Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in <strong>Figure S10B _UbiquitinPSer65.tif </strong>(Halo-multiDSK pull-down)</li> <li>GAPDH Shown in <strong>Figure S10B _GAPDH.tif </strong></li> <li>Parkin shown in <strong>FigureS10B_Parkin.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS10B_ParkinPSer65.tif</strong></li> <li>PINK1 shown in <strong>FigureS10B_PINK1.tif</strong></li> <li>OPA1 shown in <strong>FigureS10B_OPA1.tif</strong></li> </ul> <p> </p> <p><strong>Figure S11: Immunoblots for <em>in vitro</em> reconstitution assay in HeLa cells.</strong></p> <p>Scans of X-ray film:</p> <ul> <li>CISD1shown in <strong>FigureS11_CISD1.tif</strong></li> <li>CPT1α shown in <strong>FigureS11_ CPT1</strong><strong>a</strong><strong>.tif</strong></li> <li>CYB5B shown in <strong>FigureS11_CYB5B.tif</strong></li> <li>HK1 shown in <strong>FigureS11_HK1.tif</strong></li> <li>MFN2 shown in <strong>FigureS11_MFN2.tif</strong></li> <li>VDAC shown in <strong>FigureS11_VDAC.tif</strong></li> <li>Phospho-Ser65 Parkin shown in <strong>FigureS11_ParkinPSer65.tif</strong></li> <li>Parkin shown in <strong>FigureS11_Parkin.tif</strong></li> <li>Ubiquitin shown in <strong>FigureS11_Ubiquitin .tif</strong></li> <li>Phospho-Ser65 Ubiquitin shown in <strong>FigureS11_UbiquitinPSer65 .tif</strong></li> </ul> <p> </p> <p><strong>FigureS12A: Purification of CPT1</strong><strong>α protein.</strong></p> <ul> <li><strong>FigureS12A_</strong><strong> Akta_Purifier_Curves.txt</strong>.</li> </ul> <p> Tab delimited text of data from the AKTA system, plotted in figure S12A. Columns are laid out as volume/measured parameter for each parameter from the Akta. The columns plotted in figure S12A were mAU (columns A and B in excel) and fractions (columns M and N in excel).</p> <ul> <li><strong>Figure_S12A_Coomassie_Gel.tif</strong>. </li> </ul> <p>Coomassie stained gels of fractions from the AKTA system in figure S12A.</p> <p>Top: Left hand Coomassie gel, figure S12A</p> <p>Bottom: Right hand Coomassie gel, figure S12A</p> <p> </p> <p><strong>FigureS12B-C: Purification of recombinant Parkin targets </strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <ul> <li><strong>FigureS12B_Ub_Targets_High_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets alongside a BSA curve.</p> <p>Left: Lane: 1: Mwt Marker, 2: Fam213A, 3: MAO-B, 4: CAMK2α, <strong>5: MAO-A (figure S12B)</strong>, 6: GST-MAPRE2, 7: MBP-CYB5R3, 8: MBP-CYB5B, 9: MBP-CYB5B, 10: SNX3, 11: His-SUMO-MAO-B, 12: GST-MAO-B, 13: 0.03125 ug BSA, 14: 0.0625 ug BSA, 15: 0.125 ug BSA, 16: 0.25 ug BSA, 17: 0.5 ug BSA, 18: 1 ug BSA</p> <p>Right: Lane: 1: Mwt Marker, 2: GST-MAO-A, 3: GST-FAM213A, <strong>4: MBP-CAMK2</strong><strong>α</strong><strong> (figure S12B)</strong>, 5: GST-TDRKH, 6: MBP-CPT1α, 7: MBP-CYB5B, 8: CAMK2β, 9:, 10: His-SUMO-MAO-B, 11: GST-MAO-B, 12: 0.03125 ug BSA, 13: 0.0625 ug BSA, 14: 0.125 ug BSA, 15: 0.25 ug BSA, 16: 0.5 ug BSA, 17: 1 ug BSA</p> <p> </p> <ul> <li><strong>Figure_S12B_Ub_Targets_Low_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets alongside a BSA curve.</p> <p>Lane: 1: Mwt Marker, <strong>2: GST-Miro1 (figure S12B</strong>), 3: His-SUMO-Fam213A, 4: His-SUMO-MAPRE2, 5: His-SUMO-MAO-A, 6: His-SUMO-MAO-B, 7: His-SUMO-SNX26, 8: His-SNX3, 9: His-MAPRE2, 10: GST-FAM213A, 11: His-SUMO-CAMK2α, 12: His-SUMO-CAMK2β, 13: GST-FAM213A, 14: 0.03125 ug BSA, 15: 0.0625 ug BSA, 16: 0.125 ug BSA, 17: 0.25 ug BSA, 18: 0.5 ug BSA, 19: 1 ug BSA</p> <p> </p> <ul> <li><strong>Figure_S12C_Ub_Targets_800nm.tif</strong></li> </ul> <p>Coomassie stained gels of the parkin targets in duplicate alongside a BSA curve.</p> <p>Top: Lane: 1: Mwt Marker, <strong>2+3: ACSL1 45-end (Figure S12C)</strong>, <strong>4+5: SNX3 (Figure S12C),</strong> 6+7: MFN1, 8+9: MFN2, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p>Middle: Lane: 1: Mwt Marker, 2+3: CAMK2α, <strong>4+5: CAMK2</strong><strong>β</strong><strong> (Figure S12C)</strong>, 6+7: MAO-A, <strong>8+9: MAO-B (Figure S12C)</strong>, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p>Bottom: Lane: 1: Mwt Marker, 2+3: SRCIN, 4+5: His-MAPRE2, 6+7: <strong>FAM213A (Figure S12C)</strong>, 8+9: CISD1, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA</p> <p> </p> <p><strong>FigureS13-S14: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <p>Scans of X-ray film:</p> <ul> <li><strong>FigureS13A-B_S14C-D_a6His_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B</strong>), 2: <strong>MAO-B (Figure S13A</strong>), 3: MAPRE2, 4: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D)</strong></p> <p>Top: 1: <strong>CAMK2</strong><strong>α</strong><strong> (Figure S14C)</strong>, 2: Miro1, 3: <strong>MAO-A (Figure S13A)</strong></p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aFLAG_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B)</strong>, 2: MAPRE2, 3: <strong>MAO-A (Figure S13A)</strong></p> <p>Top: 1: <strong>MAO-B (Figure S13A)</strong>, 2: <strong>CAMK2</strong><strong>α</strong><strong> (Figure S14C),</strong> 3: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D)</strong>, 4: Miro1 </p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aTarget_05m00s_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: FAM213A, 2: MAPRE2, 3: MAO-A</p> <p>Top: 1: MAO-B, 2<strong>: CAMK2</strong><strong>α</strong><strong> (Figure S14C)</strong>, 3: <strong>CAMK2</strong><strong>β</strong><strong> (Figure S14D</strong>), 4: Miro1 </p> <p> </p> <ul> <li><strong>FigureS13A-B_S14C-D_aTarget_06m00s_Film.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes from left to right:</p> <p>Top: 1: <strong>FAM213A (Figure S13B)</strong>, 2: MAPRE2, 3: <strong>MAO-A (Figure S13A)</strong></p> <p>Top: 1: <strong>MAO-B (Figure S13A)</strong>, 2: CAMK2α, 3: CAMK2β, 4: Miro1 </p> <p> </p> <p><strong>FigureS13C: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>ACSL1 shown in <strong>FigureS13C_ACSL1_800nm.tif</strong></li> <li>Flag-Ub (ACSL1) shown in <strong>Figure_S13C_ACSL1_Flag_800nm.tif</strong></li> <li>His (ACSL1) shown in <strong>Figure_S13C_ACSL1_His_800nm.tif</strong></li> </ul> <p> </p> <p><strong>FigureS14B: Immunoblots for validation of Parkin-dependent substrates in<em> in vitro </em>studies.</strong></p> <p>Blots shown in the paper are highlighted in bold.</p> <p>Scans of Western blots (LI-COR):</p> <ul> <li>SNX3 shown in <strong>FigureS14B_SNX3_800nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: <strong>SNX3 (Figure S14B</strong>), FAM213A, Miro1, Miro1</p> <ul> <li>Flag-Ub (SNX3) shown in <strong>Figure_S14B_Flag_SNX3_800nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4:WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: Miro1, FAM213A, Miro1, <strong>SNX3 (Figure S14B)</strong></p> <ul> <li>His (SNX3) shown in <strong>Figure_S14B_His_SNX3_700nm.tif</strong></li> </ul> <p>Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1</p> <p>Membranes, left to right: CPT1α, MFN1, <strong>SNX3 (Figure S14B)</strong>, FAM213A</p> <p> </p> <p><strong>FigureS1: Immunocytochemistry of mouse cortical neurons.</strong></p> <ul> <li><strong>FigureS1_TILE_maximun intensity projection.tif</strong></li> </ul> <p>Original acquisition: MAP2 neuronal marker (green-488); GFAP astrocytic marker (red-594); Hoechst nuclear marker (blue 361/497). </p> <ul> <li><strong>FigureS1MGB (RGB).tif</strong></li> </ul> <p>Modified from FigureS1_TILE_maximun intensity projection.tif: MAP2 (green); GFAP (magenta); Hoechst (blue).</p> <p> </p> <p><strong>Table for Fig1DE.xlsx</strong></p> <p>Numerical data for the charts shown in Figure1D and Figure1E.</p> <p> </p> <p><strong>Table for FigS1A.xlsx</strong></p> <p> Numerical data for quantification of Map2 and GFAP in mouse cortical neurons shown in FigureS1A.</p> <p> </p> <p><strong>Table for FigS1B (Cortical neurons_DIA_Report).xlsx</strong></p> <p> Numerical data for DIA proteomic analysis of AO and DMSO treated cortical neurons shown in FigureS1B.</p> <p> </p> <p><strong>Table for FigS2B_C_Oxygraph.xlsx</strong></p> <p>Numerical data for the charts shown in FigureS2B and FigureS2C.</p> <p> </p> <p><strong>Table for Fig3A.xlsx</strong></p> <p>Numerical data for total protein abundance in PINK1 WT and KO shown in Figure3A.</p> <p> </p> <p><strong>Table for Fig3B.xlsx</strong></p> <p>Numerical data of abundance of phosphorylated-Ser65 or Ser57 shown in Figure3B.</p>
Toxicogenomic profiles of neuronal targeting insecticides in zebrafish embryos as non-target aquatic vertebrate model
<p>We have conducted semi-static exposure studies with six neuronal targeting insecticides on fertilized zebrafish (Danio rerio) eggs, similar to the OECD 236 guideline for the 96h zebrafish embryo toxicity test. The aim of these transcriptomic profiling experiments was to screen for ecotoxicogenomic fingerprints in zebrafish (Danio rerio) embryos as aquatic vertebrate non-target model exposed to sub lethal concentrations of pesticides. Data published in <a href="https://doi.org/10.1016/j.chemosphere.2021.132746">Reinwald et al. 2021</a> (PMID:<strong>34748799</strong>).</p> <p>For experimental details please refer to the publicly accessible experiment description and treatment protocols deposited in the <a href="https://www.ebi.ac.uk/arrayexpress/">ArrayExpress database </a>at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under the following accession numbers: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9852/">E-MTAB-9852</a> (Abamectin), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9855/">E-MTAB-9855 </a>(Carbaryl),<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9853/"> E-MTAB-9853</a> (Chlorpyrifos), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9854/">E-MTAB-9854</a> (Fipronil), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9859/">E-MTAB-9859</a> (Imidacloprid), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9860/">E-MTAB-9860</a> (Methoxychlor).</p> <p>The uploaded data archives (<a href="https://www.7-zip.org/">7-zip</a> compressed) consists of three major data types:<br> 1. MultiQC reports from raw RNA-Seq read processing and QC (50bp SR) ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neuotox_multiQCreports.7z">Neuotox_multiQCreports.7z </a>)<br> 2. Result tables of differential gene expression analysis (DGEA) with DESEq2 ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_DESeq2_ResultTables.7z">Neurotox_DESeq2_ResultTables.7z </a>)<br> 3. Result tables of gene set enrichment analysis (GSEA) with clusterProfiler ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_clusterProfiler_ResultTables.7z">Neurotox_clusterProfiler_ResultTables.7z </a>)<br> 4. Result tables of overrepresentation analysis (ORA) via <em>clusterProfiler::compareCluster() </em>( <a href="https://zenodo.org/api/files/c3fb14b4-6a90-4b12-a2be-ed5661d19683/Neurotox_clusterProfiler_ORA_on_core_DEGs.7z?versionId=2156d2be-b815-4157-b0f9-3eeed117b812">Neurotox_clusterProfiler_ORA_on_core_DEGs.7z </a>)</p> <p>Each data archive contains a README file describing the methods applied to generate the respective result tables / reports. For each tested substance and exposure condition a DGEA and GSEA result table is uploaded. The corresponding bash and R codes for each analysis step are available on github under:<br> <a href="https://github.com/hreinwal/zfeNeurotox">https://github.com/hreinwal/zfeNeurotox</a></p> <p>Gene count normalization and DGEA was conducted with DESeq2 (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">Love et al., 2014</a>, DOI 10.1186/s13059-014-0550-8). Three biological replicates per condition, exposure treatments were compared with respect to the control group in a pairwise fashion, applying Wald’s t-test. P values were corrected for multiple testing with independent hypothesis weighting (IHW) (<a href="https://www.nature.com/articles/nmeth.3885">Ignatiadis et al., 2016</a>, DOI 10.1038/nmeth.3885) after Benjamini-Hochberg (BH). To improve the signal to statistical noise ratio, the obtained log<sub>2</sub>-fold change (lfc) values were shrunk with the apeglm method described by Zhu and colleagues (<a href="https://academic.oup.com/bioinformatics/article/35/12/2084/5159452?login=true">2019</a>, DOI 10.1093/bioinformatics/bty895) before DGEA result tables were subjected to GSEA via clusterProfiler (<a href="https://www.liebertpub.com/doi/10.1089/omi.2011.0118">Yu et al., 2012</a>, DOI 10.1089/omi.2011.0118)<br> and reactomePA (<a href="https://pubs.rsc.org/en/content/articlehtml/2015/mb/c5mb00663e">Yu and He, 2016</a>, DOI 10.1039/C5MB00663E). The linked ArrayExpress accession numbers above, provide access to the raw and DESeq2 normalized gene count matrices upon which these analysis were performed. Genes were annotated through the biomaRt package (<a href="https://www.nature.com/articles/nprot.2009.97.pdf?origin=ppub">Durinck et al., 2009</a>, DOI 10.1038/nprot.2009.97) in R (<a href="https://www.r-project.org/">R Core Team 2021</a>).</p>
Post-hypnotic Suggestions of Safety and Neuronal Signals of Reward Sensitivity
<p>We tested 24 highly suggestible participants in two experimental conditions in an EEG study. In the beginning of the experiment, all participants were hypnotized and suggested to feel safe. The feeling of safety was associated with a post-hypnotic trigger that elicits the feeling of safety afterwards. After the termination of the hypnotic state, participants completed two experimental conditions in balanced order. In the safety condition, participants used the post-hypnotic trigger of safety and played a risk game where they could win monetary rewards. In the control condition, participants used a control trigger and played the same risk game. In a follow-up survey, we asked participants if the post-hypnotic trigger of safety still worked.</p> <p>EEG data were recorded with Brain Vision Recorder and analyzed with Matlab and EEGlab. All data were analyzed with R.</p> <p>The results are published here:</p> <p>Boehmer, J., & Schmidt, B. (2022). Safety on demand: Post-hypnotic suggestions of safety reduce neural signals of reward sensitivity with long-term beneficial effects. <em>Psychophysiology </em><a href="https://doi.org/10.1111/psyp.14015">https://doi.org/10.1111/psyp.14015</a></p>
Patch-clamp recordings from dorsal raphe neurons
<p>This dataset contains whole-cell electrophysiological recordings (patch-clamp recordings) from three cell types in mice:</p> <ul> <li>serotonin (5-HT) neurons,</li> <li>somatostatin (SOM)-expressing GABA interneurons, and</li> <li>layer 5 pyramidal neurons.</li> </ul> <p>5-HT and GABA neurons were recorded in the dorsal raphe nucleus (DRN), which is the main source of serotonergic input to the forebrain. Together, they make up the majority of the neurons found in the DRN.</p> <p>This dataset can be used to investigate the intrinsic electrophysiological properties of these two types of DRN neurons and contrast them with another abundant and well-studied cell type, the L5 pyramidal neuron.</p> <p>This data was used in <a href="https://doi.org/10.7554/eLife.72951" rel="noopener" title="Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework | eLife">our paper</a> describing a spiking neural network model of the dorsal raphe nucleus:</p> <p>Emerson F. Harkin, Michael B. Lynn, Alexandre Payeur, Jean-François Boucher, Léa Caya-Bissonnette, Dominic Cyr, Chloe Stewart, André Longtin, Richard Naud, and Jean-Claude Béïque. Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework. eLife, 2023. doi: 10.7554/eLife.72951</p>
Data supporting "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study"
<p>Data and code supporting the article "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study".</p> <p> </p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
<p>(This is the dataset used in <a href="https://doi.org/10.1101/2022.05.18.492501" rel="noopener" title="Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information">Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information</a>, Abstract below)</p> <p><span>Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using pairwise spike time statistics, co-activity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.</span></p>
Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs
<p>Data from "Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs" by Barnes SJ, Keller GB & Keck T.</p> <p> </p>
A brain-wide, annotated dataset of WFA-positive perineuronal nets and parvalbumin neurons in the adult mouse brain
<p><strong>Microscopy dataset for perineuronal nets and parvalbumin-positive interneurons in the adult mouse brain</strong></p> <p>This dataset contains the data used in the paper titled:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain</em></p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices of 7 adult mice and several kinds of biological annotations.</p> <p>For each mouse, the annotations contain information about:</p> <ul> <li>Several files related to the alignment of each brain slice to the Allen Brain Institute CCFv3 atlas (for a more detailed description see <a href="https://github.com/LeonardoLupori/brainAlignment">here</a>)</li> <li>Location of individual PNNs and PV cells in each slice</li> </ul> <p><strong>Folder Structure</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse.</p> <p>Each mouse folder contains:</p> <ol> <li>a <em>MOUSEID-info.xml</em> file - Contains general information for the mouse and images</li> <li>a <em>MOUSEID-quicknii.xml</em> file - Contains information for the alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>MOUSEID-visualign.json</em> file - Contains information for the alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>counts </em>folder - Contains annotations for PNNs and PV cell locations for each slice</li> <li>a <em>dispField </em>folder - Contains displacement fields for non-rigid alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>hiRes </em>folder - Contains original, full-resolution, experimental images</li> <li>a <em>masks </em>folder - Contains binary masks for restricting the analysis</li> <li>a <em>thumbnails </em>folder - Contains low-resolution</li> </ol> <p><strong>Files Description</strong></p> <ul> <li><em>MOUSEID-info.xml</em> <ul> <li>XML file containing information about this mouse and details on each image</li> </ul> </li> <li><em>MOUSEID-quicknii.xml</em> <ul> <li>XML file used for global alignment of all the images to the CCFv3 using the software <a href="https://www.nitrc.org/projects/quicknii">QuickNII</a></li> </ul> </li> <li><em>MOUSEID-visualign.json</em> <ul> <li>JSON file used for the interactive local non-rigid alignment of brain slices to the CCFv3 using the software <a href="https://www.nitrc.org/projects/visualign">VisuAlign</a></li> </ul> </li> <li><em>counts </em>folder <ul> <li>Folder containing two .csv files for each high-resolution image. Each .csv file contains the (x,y) location of all PNNs (channel 1) and PV cells (channel 2) detected in that image</li> </ul> </li> <li><em>dispField </em>folder <ul> <li>This folder contains displacement fields in the X and Y direction for each image. These files are meant to be loaded in MATLAB and fed to the function <a href="https://it.mathworks.com/help/images/ref/imwarp.html">imwarp</a>. This function can be used to apply a non-rigid transformation to the reference volume slices in order for it to closely match experimental images.</li> </ul> </li> <li><em>hiRes </em>folder <ul> <li>Folder containing high-resolution experimental images split by channels</li> </ul> </li> <li><em>masks </em>folder <ul> <li>Folder containing binary masks. These files are used to restrict the analysis to portions of the image containing biological tissue and to exclude areas where the tissue was damaged or presented artifacts</li> </ul> </li> <li><em>thumbnails </em>folder <ul> <li>Folder containing a low-resolution RGB version of the experimental images </li> </ul> </li> </ul> <p> </p>
An analgesic pathway from parvocellular oxytocin neurons to the periaqueductal gray in rats
<p>Raw data set related to the publication "An analgesic pathway from parvocellular oxytocin neurons to the periaqueductal gray in rats". It includes ex vivo electrophysiology, ex vivo GRAB recordings, and neuroanatomy data.</p>
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex
<p class="MsoNormal"><span>In everyday life, we integrate visual and auditory information in routine tasks such as navigation and communication. While concurrent sound can improve visual perception, the neuronal <span>correlates of audiovisual integration are not fully understood. Specifically, it remains unclear whether neuronal firing patters in the primary visual cortex (V1) of awake animals demonstrate similar sound-induced improvement in visual discriminability. Furthermore, presentation of sound is associated with movement in the subjects, but little is understood about whether and how sound-associated movement affects audiovisual integration in V1. Here, we investigated how sound and movement interact </span>to modulate V1 visual responses in awake, head-fixed mice and whether this interaction improves neuronal encoding of the visual stimulus. We presented visual drifting gratings with and without simultaneous auditory white noise to awake mice while recording mouse movement and V1 neuronal activity. Sound modulated activity of 80% of light-responsive neurons, with 95% of neurons increasing activity when the auditory stimulus was present. A generalized linear model revealed that sound and movement had distinct and complementary effects of the neuronal visual responses. Furthermore, decoding of the visual stimulus from the neuronal activity was improved with sound, an effect that persisted even when controlling for movement. These results demonstrate that sound and movement modulate visual responses in complementary ways, improving neuronal representation of the visual stimulus. This study clarifies the role of movement as a potential confound in neuronal audiovisual responses and expands our knowledge of how multimodal processing is mediated at a neuronal level in the awake brain.</span></p> <p></p>
Data from: Global change in brain state during spontaneous and forced walk in Drosophila is composed of combined activity patterns of different neuron classes
<p><span>Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult <em>Drosophila </em>to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.</span></p>
Co-activation probability between neurons in the largest brain connectome of the fruit fly
<p>This is a data set containing the co-activation probability between neurons in the largest brain connectome of the fruit fly released by the FlyEM project. The co-activation probability is measured based on neural dynamics computation, where a standard leaky integrate-and-fire (LIF) model is applied on the connectome to generate neural dynamics. Please read the paper "Yang Tian, Pei Sun; <strong>Percolation may explain efficiency, robustness, and economy of the brain</strong>. <em><em>Network Neuroscience</em></em> 2022; 6 (3): 765–790. doi: <a href="https://doi.org/10.1162/netn_a_00246">https://doi.org/10.1162/netn_a_00246</a>" for more details.</p> <p><strong>This is the newest version of the data set.</strong></p> <p>The following is a list of variable information:</p> <p>(1) SomaLocation is a 23008*3 matrix that contains the three-dimensional coordinates of neurons;</p> <p>(2) LambdaVector is the vector of a vector of synaptic excitation-inhibition (E/I) balance (see "Percolation may explain efficiency, robustness, and economy of the brain" for detailed explanations).</p> <p>(3) DirectedCoactivationPattern is a cell of co-activation probability matrices generated under each E/I balance condition, which is used in "Percolation may explain efficiency, robustness, and economy of the brain" for computational experiments. The (i,j)-th element in the matrix is the probability for neuron i to activate neuron j under the corresponding E/I balance condition. Note that the (i,j)-th element can be differnt from the (j,i)-th element. </p> <p>(4) SymmetricCoactivationPattern is a cell of symmetric co-activation probability matrices generated under each E/I balance condition. This is a new data that has not been used in "Percolation may explain efficiency, robustness, and economy of the brain" yet. The (i,j)-th element in the matrix is the probability for neurons i and j to be co-activated under the corresponding E/I balance condition. If we define D as the directed co-activation probability matrix and denote S as the symmetric co-activation probability matrix, then we have S(i,j)=S(j,i)=0.5*(D(i,j)+D(j,i)). </p> <p>The earlist version of this data can be seen in https://zenodo.org/record/5497516, which may lack detailed explanations.</p> <p>The second version of this data can be seen in https://zenodo.org/record/7869532, where a small mistake is found while calculating the SymmetricCoactivationPattern. This mistake is resolved in the newest version.</p>
Supplementary information for: Multisensory integration by polymodal sensory neurons dictates settlement in actinulae larvae
<div> <div> <div> <p>Multisensory integration (MSI) combines information from more than one sensory modality to elicit behaviors distinct from unisensory behaviors. MSI is best understood in animals with complex brains and specialized centers for parsing sensory information, but the dispersive larvae of sessile marine invertebrates utilize multimodal environmental sensory stimuli to base irreversible settlement decisions on, and most lack complex brains. Here, we examined the sensory determinants of settlement in actinula larvae of the hydrozoan <em>Ectopleura</em> <em>crocea</em> (Cnidaria), which possess a diffuse nerve net. A factorial settlement study revealed that photo-, chemo-, and mechano-sensory cues each influence the settlement response, which was complex and dependent on specific combinations of cues, therefore indicating MSI. Mechanosensory cues either inhibited or enhanced settlement rates depending on the presence or absence of chemical and light cues in the environment. Sensory gene expression over development peaked with developmental competence to settle, which in actinulae, requires cnidocyte discharge. Transcriptome analyses also highlighted several deep homological links between cnidarian and bilaterian mechano- chemo- and photo-sensory pathways. Fluorescent in situ hybridization studies of candidate transcripts suggested cellular partitioning of sensory function among the few cell types that comprise the actinula nervous system, where ubiquitous polymodal sensory neurons with putative chemo- and photo-sensitivity interface with mechanoreceptive cnidocytes. We propose that a simple multisensory processing circuit, involving polymodal chemo/photosensory neurons and mechanoreceptive cnidocytes, is sufficient to explain MSI in actinulae settlement. Our study demonstrates that MSI is not exclusive to complex brains, but likely predated and contextualized their evolution.</p> </div> </div> </div>
RNAseq data: Analysis of circRNA expression in human neuronal differentiation
<p>This dataset contains sequencing read count data related to samples from differentiating human neuroepithelial stem cells (NES) collected at days zero (NES), five (D5) and 28 (D28) of differentiation. Details on how samples were collected and how data was generated and processed are described below.</p> <p> </p> <p><em>Sample preparation</em></p> <p>NES were seeded on tissue culture flasks coated with 20 μg/ml poly-L-ornithine (Sigma-Aldrich P3655), and 1 μg/ml laminin (Sigma-Aldrich L2020). Cells were grown in DMEM/F12+GlutaMAX medium (ThermoFisher 31331093) supplemented with 0.05X B27 (ThermoFisher 17504044), 1X N2 (ThermoFisher 17502001), 10 ng/ml bFGF (fisher scientific CTP0261), 10 ng/ml EGF (PeproTech AF-100-15) and 10 U/ml penicillin/streptomycin (ThermoFisher 15140122). Medium was exchanged 50% daily and cells maintained in 5% CO2 at 37ºC, passaging once 100% confluent and seeding at a density of 5x104 cells/cm2. Neural differentiation was induced by growth factor withdrawal the day after plating with media B27 concentration increased to 0.5X. Media was exchanged 50% every second day up until D15, after which media was supplemented with 0.4 ug/ml laminin and exchanged 50% every three days. </p> <p> </p> <p><em>RNA extraction and sequencing</em></p> <p>Cells were lysed in TRIzol reagent (ThermoFischer 15596026) before separating with chloroform and mixing the aqueous phase with isopropanol as per manufacturer directions. RNA was then isolated from the isopropanol/chloroform solution using the ReliaPrep RNA Cell Miniprep kit (Promega Z6010). Libraries were prepared with Illumina Truseq Stranded total RNA RiboZero GOLD kit and sequenced on the NovaSeq6000 platform with a 2x151 setup using NovaSeqXp workflow in S4 mode flowcell.</p> <p> </p> <p><em>Data generation</em></p> <p>Raw reads were processed using cutadapt v3.2 to trim adaptor sequences and low-quality base pairs and discard short reads (options: -m 20 -e 0.1 -q 20 -O 1). The GRCh37 genome assembly was used for all alignment, annotation, and downstream analysis steps. Trimmed read weres alignment to the GRCh37 genome assembly using TopHat v2.0.9 tophat_fusion (with Bowtie v1.1.2 and Samtools v0.1.19) with –fusion-min-dist 200. BAM files have been anonymised by removal of potentially identifiable genetic variant information using BAMboozle v0.5.0 (Ziegenhain & Sandberg, 2021) with default settings. This BAM files and corresponding index (.bai) files are provided here with naming convention "<em>label.</em>bam" Information on sample labels and corresponding conditions is provided in the file 'metadata.txt'.</p> <p><br> </p>
Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus
<p>Data for paper: Directed Stepwise Tracing of Polysynaptic Neuronal Circuits With Replication-deficient Pseudorabies Virus.</p> <p>Brain functions are accomplished by polysynaptic circuits formed by neurons wired together through multiple orders of synaptic connections. Polysynaptic connectivity has been difficult to examine due to a lack of methods of continuously tracing the pathways in a controlled manner. Here we demonstrate directed, stepwise retrograde polysynaptic tracing by inducible reconstitution of replication-deficient transneuronal pseudorabies virus (PRV<sup>∆IE</sup>) in the brain. Furthermore, PRV<sup>∆IE</sup> replication can be temporally restricted to minimize its neurotoxicity. With this tool, we delineate a wiring diagram between the hippocampus and striatum-- two major brain systems for learning, memory and navigation--which consist of projections from specific hippocampal domains to specific striatal areas via distinct intermediate brain regions. Therefore, this inducible PRV<sup>∆IE </sup>system provides a tool for dissecting polysynaptic circuits underlying complex brain functions.</p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.