Abstract

Single-photon-excitation-based miniaturized microscope, or miniscope, has recently emerged as a powerful tool for imaging neural ensemble activities in freely moving animals. In the meanwhile, this highly flexible and implantable technology promises great potential for studying a broad range of cells, tissues and organs. To date, however, applications have been largely limited by the properties of the imaging modality. It is therefore highly desirable for a method generally applicable for processing miniscopy images, enabling and extending the applications to diverse anatomical and functional traits, spanning various cell types in the brain and other organs. We report an image processing approach, termed BSSE, for background suppression and signal enhancement for miniscope image processing. The BSSE method provides a simple, automatic solution to the intrinsic challenges of overlapping signals, high background and artifacts in miniscopy images. We validated the method by imaging synthetic structures and various biological samples of brain, tumor, and kidney tissues. The work represents a generally applicable tool for miniscopy technology, suggesting broader applications of the miniaturized, implantable and flexible technology for biomedical research.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (6)

J. C. Jimenez, K. Su, A. R. Goldberg, V. M. Luna, J. S. Biane, G. Ordek, P. Zhou, S. K. Ong, M. A. Wright, L. Zweifel, L. Paninski, R. Hen, and M. A. Kheirbek, “Anxiety Cells in a Hippocampal-Hypothalamic Circuit,” Neuron 97(3), 670–683 (2018).
[Crossref] [PubMed]

P. Zhou, S. L. Resendez, J. Rodriguez-Romaguera, J. C. Jimenez, S. Q. Neufeld, A. Giovannucci, J. Friedrich, E. A. Pnevmatikakis, G. D. Stuber, R. Hen, M. A. Kheirbek, B. L. Sabatini, R. E. Kass, and L. Paninski, “Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data,” eLife 7, e28728 (2018).
[Crossref] [PubMed]

J. Lu, C. Li, J. Singh-Alvarado, Z. C. Zhou, F. Fröhlich, R. Mooney, and F. Wang, “MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline,” Cell Reports 23(12), 3673–3684 (2018).
[Crossref] [PubMed]

J. T. Miyauchi, M. D. Caponegro, D. Chen, M. K. Choi, M. Li, and S. E. Tsirka, “Deletion of neuropilin 1 from microglia or bone marrow–derived macrophages slows glioma progression,” Cancer Res. 78(3), 685–694 (2018).
[Crossref] [PubMed]

W. Mau, D. W. Sullivan, N. R. Kinsky, M. E. Hasselmo, M. W. Howard, and H. Eichenbaum, “The Same Hippocampal CA1 Population Simultaneously Codes Temporal Information over Multiple Timescales,” Curr. Biol. 28(10), 1499–1508 (2018).
[Crossref] [PubMed]

S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
[Crossref] [PubMed]

2017 (4)

W. A. Liberti, L. N. Perkins, D. P. Leman, and T. J. Gardner, “An open source, wireless capable miniature microscope system,” J. Neural Eng. 14(4), 045001 (2017).
[Crossref] [PubMed]

A. Klaus, G. J. Martins, V. B. Paixao, P. Zhou, L. Paninski, and R. M. Costa, “The Spatiotemporal Organization of the Striatum Encodes Action Space,” Neuron 95(5), 1171–1180 (2017).
[Crossref] [PubMed]

K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
[Crossref] [PubMed]

A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
[Crossref] [PubMed]

2016 (8)

F. Carvalho Poyraz, E. Holzner, M. R. Bailey, J. Meszaros, L. Kenney, M. A. Kheirbek, P. D. Balsam, and C. Kellendonk, “Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action,” J. Neurosci. 36(22), 5988–6001 (2016).
[Crossref] [PubMed]

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

J. Cox, L. Pinto, and Y. Dan, “Calcium imaging of sleep-wake related neuronal activity in the dorsal pons,” Nat. Commun. 7(1), 10763 (2016).
[Crossref] [PubMed]

T. C. Harrison, L. Pinto, J. R. Brock, and Y. Dan, “Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors,” Front. Neural Circuits 10, 36 (2016).
[Crossref] [PubMed]

G. Barbera, B. Liang, L. Zhang, C. R. Gerfen, E. Culurciello, R. Chen, Y. Li, and D. T. Lin, “Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information,” Neuron 92(1), 202–213 (2016).
[Crossref] [PubMed]

E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. S. Peterka, R. Yuste, and L. Paninski, “Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data,” Neuron 89(2), 285–299 (2016).
[Crossref] [PubMed]

J. T. Miyauchi, D. Chen, M. Choi, J. C. Nissen, K. R. Shroyer, S. Djordevic, I. C. Zachary, D. Selwood, and S. E. Tsirka, “Ablation of Neuropilin 1 from glioma-associated microglia and macrophages slows tumor progression,” Oncotarget 7(9), 9801–9814 (2016).
[Crossref] [PubMed]

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7(1), 12471 (2016).
[Crossref] [PubMed]

2015 (3)

L. Pinto and Y. Dan, “Cell-Type-Specific Activity in Prefrontal Cortex during Goal-Directed Behavior,” Neuron 87(2), 437–450 (2015).
[Crossref] [PubMed]

Y. Ziv and K. K. Ghosh, “Miniature microscopes for large-scale imaging of neuronal activity in freely behaving rodents,” Curr. Opin. Neurobiol. 32, 141–147 (2015).
[Crossref] [PubMed]

J. N. Betley, S. Xu, Z. F. H. Cao, R. Gong, C. J. Magnus, Y. Yu, and S. M. Sternson, “Neurons for hunger and thirst transmit a negative-valence teaching signal,” Nature 521(7551), 180–185 (2015).
[Crossref] [PubMed]

2014 (1)

R. Maruyama, K. Maeda, H. Moroda, I. Kato, M. Inoue, H. Miyakawa, and T. Aonishi, “Detecting cells using non-negative matrix factorization on calcium imaging data,” Neural Netw. 55, 11–19 (2014).
[Crossref] [PubMed]

2013 (1)

Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
[Crossref] [PubMed]

2012 (2)

2011 (3)

W. M. Lee and S. H. Yun, “Adaptive aberration correction of GRIN lenses for confocal endomicroscopy,” Opt. Lett. 36(23), 4608–4610 (2011).
[Crossref] [PubMed]

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
[Crossref] [PubMed]

H. Zhai, F. L. Heppner, and S. E. Tsirka, “Microglia/macrophages promote glioma progression,” Glia 59(3), 472–485 (2011).
[Crossref] [PubMed]

2009 (2)

E. A. Mukamel, A. Nimmerjahn, and M. J. Schnitzer, “Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data,” Neuron 63(6), 747–760 (2009).
[Crossref] [PubMed]

R. P. J. Barretto, B. Messerschmidt, and M. J. Schnitzer, “In vivo fluorescence imaging with high-resolution microlenses,” Nat. Methods 6(7), 511–512 (2009).
[Crossref] [PubMed]

2008 (1)

B. A. Flusberg, A. Nimmerjahn, E. D. Cocker, E. A. Mukamel, R. P. J. Barretto, T. H. Ko, L. D. Burns, J. C. Jung, and M. J. Schnitzer, “High-speed, miniaturized fluorescence microscopy in freely moving mice,” Nat. Methods 5(11), 935–938 (2008).
[Crossref] [PubMed]

2007 (1)

J. Reidl, J. Starke, D. B. Omer, A. Grinvald, and H. Spors, “Independent component analysis of high-resolution imaging data identifies distinct functional domains,” Neuroimage 34(1), 94–108 (2007).
[Crossref] [PubMed]

2006 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” Proc. SPIE-IS&T Electron. Imaging 6064, 606414 (2006).

2003 (1)

R. T. Sasmono, D. Oceandy, J. W. Pollard, W. Tong, P. Pavli, B. J. Wainwright, M. C. Ostrowski, S. R. Himes, and D. A. Hume, “A macrophage colony-stimulating factor receptor-green fluorescent protein transgene is expressed throughout the mononuclear phagocyte system of the mouse,” Blood 101(3), 1155–1163 (2003).
[Crossref] [PubMed]

1997 (1)

M. Okabe, M. Ikawa, K. Kominami, T. Nakanishi, and Y. Nishimune, “‘Green mice’ as a source of ubiquitous green cells,” FEBS Lett. 407(3), 313–319 (1997).
[Crossref] [PubMed]

1986 (1)

J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian Kernel for Scale-Space Filterng,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8, 26–33 (1986).
[Crossref]

1979 (1)

N. Otsu, “A threshold selection method from gray level histograms,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

Aharoni, D.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

Ahrens, M.

E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. S. Peterka, R. Yuste, and L. Paninski, “Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data,” Neuron 89(2), 285–299 (2016).
[Crossref] [PubMed]

Ahrens, S.

K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
[Crossref] [PubMed]

Albrecht, D.

S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
[Crossref] [PubMed]

Alcala Morales, P. L.

A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
[Crossref] [PubMed]

Aonishi, T.

R. Maruyama, K. Maeda, H. Moroda, I. Kato, M. Inoue, H. Miyakawa, and T. Aonishi, “Detecting cells using non-negative matrix factorization on calcium imaging data,” Neural Netw. 55, 11–19 (2014).
[Crossref] [PubMed]

Ashdown, G.

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7(1), 12471 (2016).
[Crossref] [PubMed]

Babaud, J.

J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian Kernel for Scale-Space Filterng,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8, 26–33 (1986).
[Crossref]

Bailey, M. R.

F. Carvalho Poyraz, E. Holzner, M. R. Bailey, J. Meszaros, L. Kenney, M. A. Kheirbek, P. D. Balsam, and C. Kellendonk, “Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action,” J. Neurosci. 36(22), 5988–6001 (2016).
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F. Carvalho Poyraz, E. Holzner, M. R. Bailey, J. Meszaros, L. Kenney, M. A. Kheirbek, P. D. Balsam, and C. Kellendonk, “Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action,” J. Neurosci. 36(22), 5988–6001 (2016).
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Barbera, G.

G. Barbera, B. Liang, L. Zhang, C. R. Gerfen, E. Culurciello, R. Chen, Y. Li, and D. T. Lin, “Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information,” Neuron 92(1), 202–213 (2016).
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Barretto, R. P. J.

R. P. J. Barretto, B. Messerschmidt, and M. J. Schnitzer, “In vivo fluorescence imaging with high-resolution microlenses,” Nat. Methods 6(7), 511–512 (2009).
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B. A. Flusberg, A. Nimmerjahn, E. D. Cocker, E. A. Mukamel, R. P. J. Barretto, T. H. Ko, L. D. Burns, J. C. Jung, and M. J. Schnitzer, “High-speed, miniaturized fluorescence microscopy in freely moving mice,” Nat. Methods 5(11), 935–938 (2008).
[Crossref] [PubMed]

Baudin, M.

J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian Kernel for Scale-Space Filterng,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8, 26–33 (1986).
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Baumgaertel, K.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
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Betley, J. N.

J. N. Betley, S. Xu, Z. F. H. Cao, R. Gong, C. J. Magnus, Y. Yu, and S. M. Sternson, “Neurons for hunger and thirst transmit a negative-valence teaching signal,” Nature 521(7551), 180–185 (2015).
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Biane, J.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

Biane, J. S.

J. C. Jimenez, K. Su, A. R. Goldberg, V. M. Luna, J. S. Biane, G. Ordek, P. Zhou, S. K. Ong, M. A. Wright, L. Zweifel, L. Paninski, R. Hen, and M. A. Kheirbek, “Anxiety Cells in a Hippocampal-Hypothalamic Circuit,” Neuron 97(3), 670–683 (2018).
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T. C. Harrison, L. Pinto, J. R. Brock, and Y. Dan, “Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors,” Front. Neural Circuits 10, 36 (2016).
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E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. S. Peterka, R. Yuste, and L. Paninski, “Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data,” Neuron 89(2), 285–299 (2016).
[Crossref] [PubMed]

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Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
[Crossref] [PubMed]

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
[Crossref] [PubMed]

B. A. Flusberg, A. Nimmerjahn, E. D. Cocker, E. A. Mukamel, R. P. J. Barretto, T. H. Ko, L. D. Burns, J. C. Jung, and M. J. Schnitzer, “High-speed, miniaturized fluorescence microscopy in freely moving mice,” Nat. Methods 5(11), 935–938 (2008).
[Crossref] [PubMed]

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D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

Cao, Z. F. H.

J. N. Betley, S. Xu, Z. F. H. Cao, R. Gong, C. J. Magnus, Y. Yu, and S. M. Sternson, “Neurons for hunger and thirst transmit a negative-valence teaching signal,” Nature 521(7551), 180–185 (2015).
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Caponegro, M. D.

J. T. Miyauchi, M. D. Caponegro, D. Chen, M. K. Choi, M. Li, and S. E. Tsirka, “Deletion of neuropilin 1 from microglia or bone marrow–derived macrophages slows glioma progression,” Cancer Res. 78(3), 685–694 (2018).
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Carvalho Poyraz, F.

F. Carvalho Poyraz, E. Holzner, M. R. Bailey, J. Meszaros, L. Kenney, M. A. Kheirbek, P. D. Balsam, and C. Kellendonk, “Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action,” J. Neurosci. 36(22), 5988–6001 (2016).
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J. T. Miyauchi, M. D. Caponegro, D. Chen, M. K. Choi, M. Li, and S. E. Tsirka, “Deletion of neuropilin 1 from microglia or bone marrow–derived macrophages slows glioma progression,” Cancer Res. 78(3), 685–694 (2018).
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J. T. Miyauchi, D. Chen, M. Choi, J. C. Nissen, K. R. Shroyer, S. Djordevic, I. C. Zachary, D. Selwood, and S. E. Tsirka, “Ablation of Neuropilin 1 from glioma-associated microglia and macrophages slows tumor progression,” Oncotarget 7(9), 9801–9814 (2016).
[Crossref] [PubMed]

Chen, R.

G. Barbera, B. Liang, L. Zhang, C. R. Gerfen, E. Culurciello, R. Chen, Y. Li, and D. T. Lin, “Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information,” Neuron 92(1), 202–213 (2016).
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J. T. Miyauchi, D. Chen, M. Choi, J. C. Nissen, K. R. Shroyer, S. Djordevic, I. C. Zachary, D. Selwood, and S. E. Tsirka, “Ablation of Neuropilin 1 from glioma-associated microglia and macrophages slows tumor progression,” Oncotarget 7(9), 9801–9814 (2016).
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Choi, M. K.

J. T. Miyauchi, M. D. Caponegro, D. Chen, M. K. Choi, M. Li, and S. E. Tsirka, “Deletion of neuropilin 1 from microglia or bone marrow–derived macrophages slows glioma progression,” Cancer Res. 78(3), 685–694 (2018).
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Cocker, E. D.

Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
[Crossref] [PubMed]

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
[Crossref] [PubMed]

B. A. Flusberg, A. Nimmerjahn, E. D. Cocker, E. A. Mukamel, R. P. J. Barretto, T. H. Ko, L. D. Burns, J. C. Jung, and M. J. Schnitzer, “High-speed, miniaturized fluorescence microscopy in freely moving mice,” Nat. Methods 5(11), 935–938 (2008).
[Crossref] [PubMed]

Conzelmann, K. K.

A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
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A. Klaus, G. J. Martins, V. B. Paixao, P. Zhou, L. Paninski, and R. M. Costa, “The Spatiotemporal Organization of the Striatum Encodes Action Space,” Neuron 95(5), 1171–1180 (2017).
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Cox, J.

J. Cox, L. Pinto, and Y. Dan, “Calcium imaging of sleep-wake related neuronal activity in the dorsal pons,” Nat. Commun. 7(1), 10763 (2016).
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S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7(1), 12471 (2016).
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Culurciello, E.

G. Barbera, B. Liang, L. Zhang, C. R. Gerfen, E. Culurciello, R. Chen, Y. Li, and D. T. Lin, “Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information,” Neuron 92(1), 202–213 (2016).
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” Proc. SPIE-IS&T Electron. Imaging 6064, 606414 (2006).

Dan, Y.

J. Cox, L. Pinto, and Y. Dan, “Calcium imaging of sleep-wake related neuronal activity in the dorsal pons,” Nat. Commun. 7(1), 10763 (2016).
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T. C. Harrison, L. Pinto, J. R. Brock, and Y. Dan, “Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors,” Front. Neural Circuits 10, 36 (2016).
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L. Pinto and Y. Dan, “Cell-Type-Specific Activity in Prefrontal Cortex during Goal-Directed Behavior,” Neuron 87(2), 437–450 (2015).
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K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
[Crossref] [PubMed]

Djordevic, S.

J. T. Miyauchi, D. Chen, M. Choi, J. C. Nissen, K. R. Shroyer, S. Djordevic, I. C. Zachary, D. Selwood, and S. E. Tsirka, “Ablation of Neuropilin 1 from glioma-associated microglia and macrophages slows tumor progression,” Oncotarget 7(9), 9801–9814 (2016).
[Crossref] [PubMed]

Douglass, A. M.

A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
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J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian Kernel for Scale-Space Filterng,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8, 26–33 (1986).
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” Proc. SPIE-IS&T Electron. Imaging 6064, 606414 (2006).

Eichenbaum, H.

W. Mau, D. W. Sullivan, N. R. Kinsky, M. E. Hasselmo, M. W. Howard, and H. Eichenbaum, “The Same Hippocampal CA1 Population Simultaneously Codes Temporal Information over Multiple Timescales,” Curr. Biol. 28(10), 1499–1508 (2018).
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El Gamal, A.

Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
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K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
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Flores, S. E.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

Flusberg, B. A.

B. A. Flusberg, A. Nimmerjahn, E. D. Cocker, E. A. Mukamel, R. P. J. Barretto, T. H. Ko, L. D. Burns, J. C. Jung, and M. J. Schnitzer, “High-speed, miniaturized fluorescence microscopy in freely moving mice,” Nat. Methods 5(11), 935–938 (2008).
[Crossref] [PubMed]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” Proc. SPIE-IS&T Electron. Imaging 6064, 606414 (2006).

Friedrich, J.

P. Zhou, S. L. Resendez, J. Rodriguez-Romaguera, J. C. Jimenez, S. Q. Neufeld, A. Giovannucci, J. Friedrich, E. A. Pnevmatikakis, G. D. Stuber, R. Hen, M. A. Kheirbek, B. L. Sabatini, R. E. Kass, and L. Paninski, “Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data,” eLife 7, e28728 (2018).
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Fröhlich, F.

J. Lu, C. Li, J. Singh-Alvarado, Z. C. Zhou, F. Fröhlich, R. Mooney, and F. Wang, “MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline,” Cell Reports 23(12), 3673–3684 (2018).
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Gamal, A. E.

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
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Gao, Y.

E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. S. Peterka, R. Yuste, and L. Paninski, “Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data,” Neuron 89(2), 285–299 (2016).
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W. A. Liberti, L. N. Perkins, D. P. Leman, and T. J. Gardner, “An open source, wireless capable miniature microscope system,” J. Neural Eng. 14(4), 045001 (2017).
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G. Barbera, B. Liang, L. Zhang, C. R. Gerfen, E. Culurciello, R. Chen, Y. Li, and D. T. Lin, “Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information,” Neuron 92(1), 202–213 (2016).
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Y. Ziv and K. K. Ghosh, “Miniature microscopes for large-scale imaging of neuronal activity in freely behaving rodents,” Curr. Opin. Neurobiol. 32, 141–147 (2015).
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Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
[Crossref] [PubMed]

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. E. Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
[Crossref] [PubMed]

Giovannucci, A.

P. Zhou, S. L. Resendez, J. Rodriguez-Romaguera, J. C. Jimenez, S. Q. Neufeld, A. Giovannucci, J. Friedrich, E. A. Pnevmatikakis, G. D. Stuber, R. Hen, M. A. Kheirbek, B. L. Sabatini, R. E. Kass, and L. Paninski, “Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data,” eLife 7, e28728 (2018).
[Crossref] [PubMed]

Goldberg, A. R.

J. C. Jimenez, K. Su, A. R. Goldberg, V. M. Luna, J. S. Biane, G. Ordek, P. Zhou, S. K. Ong, M. A. Wright, L. Zweifel, L. Paninski, R. Hen, and M. A. Kheirbek, “Anxiety Cells in a Hippocampal-Hypothalamic Circuit,” Neuron 97(3), 670–683 (2018).
[Crossref] [PubMed]

Golshani, P.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref] [PubMed]

Gong, L.

K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
[Crossref] [PubMed]

Gong, R.

J. N. Betley, S. Xu, Z. F. H. Cao, R. Gong, C. J. Magnus, Y. Yu, and S. M. Sternson, “Neurons for hunger and thirst transmit a negative-valence teaching signal,” Nature 521(7551), 180–185 (2015).
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J. Reidl, J. Starke, D. B. Omer, A. Grinvald, and H. Spors, “Independent component analysis of high-resolution imaging data identifies distinct functional domains,” Neuroimage 34(1), 94–108 (2007).
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A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
[Crossref] [PubMed]

Gustafsson, N.

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7(1), 12471 (2016).
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Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, “Long-term dynamics of CA1 hippocampal place codes,” Nat. Neurosci. 16(3), 264–266 (2013).
[Crossref] [PubMed]

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T. C. Harrison, L. Pinto, J. R. Brock, and Y. Dan, “Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors,” Front. Neural Circuits 10, 36 (2016).
[Crossref] [PubMed]

Hasselmo, M. E.

W. Mau, D. W. Sullivan, N. R. Kinsky, M. E. Hasselmo, M. W. Howard, and H. Eichenbaum, “The Same Hippocampal CA1 Population Simultaneously Codes Temporal Information over Multiple Timescales,” Curr. Biol. 28(10), 1499–1508 (2018).
[Crossref] [PubMed]

He, M.

K. Yu, S. Ahrens, X. Zhang, H. Schiff, C. Ramakrishnan, L. Fenno, K. Deisseroth, F. Zhao, M. H. Luo, L. Gong, M. He, P. Zhou, L. Paninski, and B. Li, “The central amygdala controls learning in the lateral amygdala,” Nat. Neurosci. 20(12), 1680–1685 (2017).
[Crossref] [PubMed]

Hen, R.

J. C. Jimenez, K. Su, A. R. Goldberg, V. M. Luna, J. S. Biane, G. Ordek, P. Zhou, S. K. Ong, M. A. Wright, L. Zweifel, L. Paninski, R. Hen, and M. A. Kheirbek, “Anxiety Cells in a Hippocampal-Hypothalamic Circuit,” Neuron 97(3), 670–683 (2018).
[Crossref] [PubMed]

P. Zhou, S. L. Resendez, J. Rodriguez-Romaguera, J. C. Jimenez, S. Q. Neufeld, A. Giovannucci, J. Friedrich, E. A. Pnevmatikakis, G. D. Stuber, R. Hen, M. A. Kheirbek, B. L. Sabatini, R. E. Kass, and L. Paninski, “Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data,” eLife 7, e28728 (2018).
[Crossref] [PubMed]

Henriques, R.

S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
[Crossref] [PubMed]

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7(1), 12471 (2016).
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F. Carvalho Poyraz, E. Holzner, M. R. Bailey, J. Meszaros, L. Kenney, M. A. Kheirbek, P. D. Balsam, and C. Kellendonk, “Decreasing Striatopallidal Pathway Function Enhances Motivation by Energizing the Initiation of Goal-Directed Action,” J. Neurosci. 36(22), 5988–6001 (2016).
[Crossref] [PubMed]

Howard, M. W.

W. Mau, D. W. Sullivan, N. R. Kinsky, M. E. Hasselmo, M. W. Howard, and H. Eichenbaum, “The Same Hippocampal CA1 Population Simultaneously Codes Temporal Information over Multiple Timescales,” Curr. Biol. 28(10), 1499–1508 (2018).
[Crossref] [PubMed]

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Hume, D. A.

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M. Okabe, M. Ikawa, K. Kominami, T. Nakanishi, and Y. Nishimune, “‘Green mice’ as a source of ubiquitous green cells,” FEBS Lett. 407(3), 313–319 (1997).
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J. Lu, C. Li, J. Singh-Alvarado, Z. C. Zhou, F. Fröhlich, R. Mooney, and F. Wang, “MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline,” Cell Reports 23(12), 3673–3684 (2018).
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A. M. Douglass, H. Kucukdereli, M. Ponserre, M. Markovic, J. Gründemann, C. Strobel, P. L. Alcala Morales, K. K. Conzelmann, A. Lüthi, and R. Klein, “Central amygdala circuits modulate food consumption through a positive-valence mechanism,” Nat. Neurosci. 20(10), 1384–1394 (2017).
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A. Klaus, G. J. Martins, V. B. Paixao, P. Zhou, L. Paninski, and R. M. Costa, “The Spatiotemporal Organization of the Striatum Encodes Action Space,” Neuron 95(5), 1171–1180 (2017).
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R. Maruyama, K. Maeda, H. Moroda, I. Kato, M. Inoue, H. Miyakawa, and T. Aonishi, “Detecting cells using non-negative matrix factorization on calcium imaging data,” Neural Netw. 55, 11–19 (2014).
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W. Mau, D. W. Sullivan, N. R. Kinsky, M. E. Hasselmo, M. W. Howard, and H. Eichenbaum, “The Same Hippocampal CA1 Population Simultaneously Codes Temporal Information over Multiple Timescales,” Curr. Biol. 28(10), 1499–1508 (2018).
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D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
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S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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R. Maruyama, K. Maeda, H. Moroda, I. Kato, M. Inoue, H. Miyakawa, and T. Aonishi, “Detecting cells using non-negative matrix factorization on calcium imaging data,” Neural Netw. 55, 11–19 (2014).
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Figures (15)

Fig. 1
Fig. 1 System setup and characterization. (a) Solidworks design scheme. (b) Comparison of the 3D-printed miniscope with a U.S. quarter coin. The LED illuminates blue light peaked at 488 nm. The indicator on the camera sensor emits red light when turned on. (c) Representative point-spread function (PSF) of the miniscope, taken with a 200-nm fluorescent bead, exhibits FWHM values of 3.6 µm and 33 µm in the lateral and axial dimensions, respectively. (d) Image of a 1951 USAF target, with fluorescent tapes attached to the rear surface. (e) Cross-sectional profile along the solid line in the inset, which shows the zoomed-in image of the boxed region in (d). The profile resolves the caliber lines separated by a known distance of 8.8 µm over 9 pixels (physical pixel size = 6 μm), determining the ~6x magnification of the system. Scale bars: 5 mm (a), 3 μm (c), 50 μm (d), 10 μm (e).
Fig. 2
Fig. 2 Architecture of BSSE. The algorithm contains two main modules. The first module is used to remove the background. Specifically, the raw image IRaw is first moderately smoothed by time-averaging or Gaussian convolution (standard deviation = 1 pixel), obtaining the image I0. The algorithm then subtracts the predominant low-frequency background IM from the image I0 using morphological image processing, obtaining the image IB1 = I0 - IM. Next, a Gaussian filter (standard deviation = 1 pixel) is applied to generate the baseline low-frequency image IGauss, and the high-frequency component of the signal IB2 is obtained using the BM3D method [35] as IB2 = I0 - IGauss. IB2 is then binarized, smoothed and normalized to generate the weight mask IW. The algorithm then modulates IB1 with the weight mask IW to remove the frequency components responsible for the fluctuating background, obtaining the background-suppressed image IBF = IB1 · IW. The second module is used to enhance the signals of the image IBF. Specifically, the first-derivative image IG1 is generated and subtracted from the image IBF, obtaining the sharpened signal image IS = IBF – σ · IG1, where σ is scale factor, determined as the ratio between the values of the simulated Gaussian PSF and its first derivative at the inflection point. IS is given a threshold to zero negative pixel values. The second-derivative image IG2 of IBF is next generated to identify and segment the crossings of the overlapping signals in IS by zero-concavity analysis, obtaining the final image IBSSE. Images were taken using 1-μm fluorescent beads. Scale bar: 5 μm.
Fig. 3
Fig. 3 Characterization of the BSSE method. (a) Raw and BSSE-processed images of 1-µm fluorescent beads, respectively. Additional Gaussian noisy background was added to the raw image. It can be observed that the fluctuating background was substantially suppressed. (b) Gaussian fitted cross-sectional profiles along the solid line in (a) of both raw and processed images, which showed enhanced signals using BSSE. (c, d) Raw (c) and BSSE-processed (d) images of 200-nm fluorescent beads. (e-h) Zoomed-in images the corresponding boxed regions in (c, d). (i) Cross-sectional profiles with respect to the corresponding solid line in (h) in the raw and processed images, showing enhanced signals of two nearby emitters separated <5 µm. Scale bars: 5 µm (a), 50 µm (c, d), 5 µm (e-h).
Fig. 4
Fig. 4 Imaging mouse brain tumor. (a,b) Raw (a) and BSSE-processed (b) images of mouse brain tumor tissue after implantation of glioma GL261-EGFP cells. (c) Merged image of (a,b), showing the suppressed background and enhanced signals using BSSE. (d-g) Zoomed-in raw (d, f) and BSSE-processed (e,g) images of the corresponding boxed regions in (a,b). (h,i) Cross-sectional profiles along the solid lines in (d,e) and (f,g), respectively, exhibit enhanced resolution of cellular structures of the tumor tissue. RSP = 0.773. Scale bars: 100 µm (a), 15 µm (d).
Fig. 5
Fig. 5 Imaging mouse kidney tissue. (a,b) Raw (a) and BSSE-processed (b) images of the kidney cortex of beta actin-EGFP mice. (c) Merged image of (a, b), showing enhanced tubular structures with suppressed background using BSSE. RSP = 0.879. (d,e) Raw (d) and BSSE-processed (e) images of the kidney medulla of beta actin-EGFP mice. (f) Merged image of (d,e). (g-j) Zoomed-in images of the corresponding boxed regions in (d,e). (k,l) Cross-sectional profiles along the solid lines in (g, h) and (i, j), respectively, exhibiting enhanced resolution of cellular structures. The arrows in (h) indicate a well-resolved structure separated as close as 3.8 µm. RSP = 0.827. Scale bars: 100 µm (a,d), 15 µm (a inset and g).
Fig. 6
Fig. 6 In vivo transient calcium imaging in freely behaving mice. (a) Left, example of the field of view of CA1 using the miniscope, displayed as the maximum temporal projection of fluorescence activity. Middle and right, the maximum projections of the frames individually processed by BSSE and MIN1PIPE, respectively. (b) The identified ROI contours superimposed on the corresponding boxed regions in (a), respectively. Left and middle, the contours were identified manually from the BSSE-processed data (a, middle). Right, the contours were identified using MIN1PIPE. (c,e,g,i,k,m) Six zoomed-in (top panel) and their contrast-adjusted (bottom panel) raw, BSSE-processed and MIN1PIPE-processed images of the corresponding ROI regions as marked in (b). (d,f,h,j,l,n) The corresponding normalized temporal fluorescence traces of six ROI examples as marked in (b). The shaded zones represent the false traces due to the cross-talk between neighboring neurons in the raw data, which were corrected by BSSE and MIN1PIPE. Scale bars: 100 µm (a), 50 µm (b), 10 µm (c), 5 sec (j).
Fig. 7
Fig. 7 (a) The PSF of the miniscope using an achromatic lens as suggested by the open-source protocol. (b) The images were taken with a 200-nm fluorescent bead, exhibits FWHM values of 3.8 µm (left) and 39 µm (right) in the lateral and axial dimensions, respectively, showing slightly broadened PSF profiles compared to the profiles using an aspheric lens in Fig. 1. Scale bar: 3 μm.
Fig. 8
Fig. 8 Illustration of the BSSE algorithm using beta-actin-EGFP mouse kidney tissue. (a-c) Architecture of the BSSE algorithm and BSSE-processed images of the kidney cortex of beta-actin-EGFP mice. As described in detail in Fig. 2, the results illustrate that the two main modules of the algorithm suppress the background (b) in the image IRaw, obtaining the image IBF, and enhance the signals (c), thus to obtain the final image IBSSE. (d) Cross-sectional profiles in the image IBF and its first-derivative image IG1 along the corresponding solid line in IG1. (e) Cross-sectional profiles along the solid color lines in IS and IG2, where IS represents the difference image between IBF and IG1, and IG2 is the second-derivative image of IBF. Concavity analysis is conducted to identify and segment the crossings of the overlapping signals (e.g. the shaded regions in (e)), obtaining the final image IBSSE. (f) Cross-sectional profiles in the images IRaw and IBSSE along the corresponding solid line in IBSSE. Scale bars: 100 µm (a, top row), 50 µm (a, left of the second row), 10 µm (a, right of the second row).
Fig. 9
Fig. 9 BSSE processing of synthetic caliber patterns. (a) Left to right, simulated raw image (left, IRaw), intermediate background-suppressed image (middle, IBF), and the final BSSE-processed image (right, IBSSE). (b) Intensity profiles of the images IRaw, IBF and IBSSE along the corresponding line in (a). The solid red lines in (b) denote the ground truth of the line positions of the pattern. The intervals between the two nearby lines start at 1 pixel and are constantly increased by 1 pixel from left to right. The two lines separated by 3 pixels were resolved in the image IBSSE but not in the images IRaw and IBF.
Fig. 10
Fig. 10 BSSE processing of the same synthetic caliber patterns as in Fig. 8 with varying SNRs. (a) Top, simulated raw image with PSNR = 61.58 dB. Bottom, the BSSE-processed image. The SSIM values compared to the ground truth are 0.1787 and 0.4581 for the raw and processed images, respectively. (b) Intensity profiles of the images along the corresponding lines in (a). (c) Top, simulated raw image with PSNR = 59.78 dB. Bottom, the BSSE-processed image. The SSIM values compared to the ground truth are 0.1319 and 0.3768 for the raw and processed images, respectively. (d) Intensity profiles of the images along the corresponding lines in (c).
Fig. 11
Fig. 11 BSSE processing of synthetic caliber patterns with varying distances. (a, c, e) The simulated raw (left) and BSSE-processed (right) images of two lines separated by 1 pixel (a), 2 pixels (b), and 3 pixels (c), respectively. (b, d, f) The intensity profiles of the images along the corresponding lines in (a, c, e), respectively. The results show that BSSE enhances the image quality by improving the SNR of the diffraction-limited images. A distance of 2 pixels is related to the diffraction limit in practice.
Fig. 12
Fig. 12 BSSE processing of synthetic caliber patterns with varying intensity. (a) Simulated underlying pattern with linearly varying intensity. (b,d,f) The simulated raw (top) and BSSE-processed (bottom) images of the pattern added with three different random noise patterns. (c, e, g) The intensity profiles of the images along the corresponding lines in (b,d,f), respectively. (h) BSSE processing of the same synthetic caliber pattern without noise. (i) Average peak intensity of BSSE-processed images (bottom panels in (b,d,f)) (green dots and error bars) and the peak intensity of the profile corresponding to the red line in (h) (gray dots). The green dots represent the mean value of BSSE-processed profiles of each row of pixels in (b,d,f) and the error bars stand for the standard deviation of these pixel values at each peak. The solid black line shows the linear relationship. Although the linear trend is largely retained, deviations from the linear relationship within the pattern can be observed for the BSSE-processed images with noisy raw data when the intensity becomes strong or weak. In contrast, the BSSE-processed image with noise-free raw data maintains acceptable linear relationship.
Fig. 13
Fig. 13 BSSE processing of synthetic caliber patterns. (a) Raw image of synthetic Siemens target caliber pattern with non-parallel and crossing structures. (b) BSSE-processed image. A gap can be observed near the center strong intensity region, because the algorithm processes the region as the background of the crossing region. (c) Merged image of the raw (a) and BSSE-processed (b) images. (d) Angular intensity plot of BSSE-processed image and the corresponding raw image along the red circle. BSSE shows distinct 12 caliber bars whereas raw have less resolved intensity peaks. (e) Intermediate signal-sharpened image IS’ using a halved the scale factor 0.5 × σ (see Fig. 2 caption), recovering the gap region as in (b). (f) Merged image of the original BSSE-processed (b) and adjusted intermediate image (e), showing the high-resolution structure without compromising background-like information.
Fig. 14
Fig. 14 Comparison with blind-deconvolution image processing. Left panel, raw (a), deconvolved (b), and BSSE-processed (c) images of the kidney cortex of beta actin-EGFP mice as in Fig. 5(a). Right panels, zoomed-in images of the corresponding color boxed regions. It can be observed that although deconvolution (10 iterations) sharpens the images, BSSE further reduces the background and enhances the signals. Scale bars: 100 µm (a, left), 15 µm (a, right).
Fig. 15
Fig. 15 Imaging and BSSE processing of Csf1r-EGFP mouse brain tissue. (a) Merged image of the raw and BSSE-processed images of Csf1r-EGFP mouse brain tissue. Csf1r-driven GFP fluorescence primarily labels microglial cells in the brain. (b) Zoomed-in raw (top) and BSSE-processed (bottom) images of the corresponding boxed region in (a). (c) Cross-sectional profiles of the raw and BSSE-processed images along the solid lines in (b). The results demonstrate enhanced cellular-level resolution of microglial structures, which otherwise would not be observed due to the strong background from the out-of-focus tissue and blood vessels (e.g. in (b)). RSP = 0.749. Scale bars: 100 µm (a), 15 µm (b).

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