Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets
Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Diehl, Markus I. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS - 2012, ECR, Orlando, Fla |
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| Übergeordnetes Werk: |
volume:393 ; year:2020 ; number:2 ; day:15 ; month:08 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.yexcr.2020.112014 |
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| 520 | |a Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. | ||
| 520 | |a Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. | ||
| 650 | 7 | |a Immune cell interactions |2 Elsevier | |
| 650 | 7 | |a Macro |2 Elsevier | |
| 650 | 7 | |a Cell cluster analysis |2 Elsevier | |
| 650 | 7 | |a Cancer |2 Elsevier | |
| 700 | 1 | |a Wolf, Steven P. |4 oth | |
| 700 | 1 | |a Bindokas, Vytas P. |4 oth | |
| 700 | 1 | |a Schreiber, Hans |4 oth | |
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10.1016/j.yexcr.2020.112014 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001092.pica (DE-627)ELV050987011 (ELSEVIER)S0014-4827(20)30236-6 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.44 bkl Diehl, Markus I. verfasserin aut Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Immune cell interactions Elsevier Macro Elsevier Cell cluster analysis Elsevier Cancer Elsevier Wolf, Steven P. oth Bindokas, Vytas P. oth Schreiber, Hans oth Enthalten in Academic Press 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS 2012 ECR Orlando, Fla (DE-627)ELV011050691 volume:393 year:2020 number:2 day:15 month:08 pages:0 https://doi.org/10.1016/j.yexcr.2020.112014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.44 Parasitologie Medizin VZ AR 393 2020 2 15 0815 0 |
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10.1016/j.yexcr.2020.112014 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001092.pica (DE-627)ELV050987011 (ELSEVIER)S0014-4827(20)30236-6 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.44 bkl Diehl, Markus I. verfasserin aut Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Immune cell interactions Elsevier Macro Elsevier Cell cluster analysis Elsevier Cancer Elsevier Wolf, Steven P. oth Bindokas, Vytas P. oth Schreiber, Hans oth Enthalten in Academic Press 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS 2012 ECR Orlando, Fla (DE-627)ELV011050691 volume:393 year:2020 number:2 day:15 month:08 pages:0 https://doi.org/10.1016/j.yexcr.2020.112014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.44 Parasitologie Medizin VZ AR 393 2020 2 15 0815 0 |
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10.1016/j.yexcr.2020.112014 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001092.pica (DE-627)ELV050987011 (ELSEVIER)S0014-4827(20)30236-6 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.44 bkl Diehl, Markus I. verfasserin aut Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Immune cell interactions Elsevier Macro Elsevier Cell cluster analysis Elsevier Cancer Elsevier Wolf, Steven P. oth Bindokas, Vytas P. oth Schreiber, Hans oth Enthalten in Academic Press 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS 2012 ECR Orlando, Fla (DE-627)ELV011050691 volume:393 year:2020 number:2 day:15 month:08 pages:0 https://doi.org/10.1016/j.yexcr.2020.112014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.44 Parasitologie Medizin VZ AR 393 2020 2 15 0815 0 |
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10.1016/j.yexcr.2020.112014 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001092.pica (DE-627)ELV050987011 (ELSEVIER)S0014-4827(20)30236-6 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.44 bkl Diehl, Markus I. verfasserin aut Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Immune cell interactions Elsevier Macro Elsevier Cell cluster analysis Elsevier Cancer Elsevier Wolf, Steven P. oth Bindokas, Vytas P. oth Schreiber, Hans oth Enthalten in Academic Press 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS 2012 ECR Orlando, Fla (DE-627)ELV011050691 volume:393 year:2020 number:2 day:15 month:08 pages:0 https://doi.org/10.1016/j.yexcr.2020.112014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.44 Parasitologie Medizin VZ AR 393 2020 2 15 0815 0 |
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10.1016/j.yexcr.2020.112014 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001092.pica (DE-627)ELV050987011 (ELSEVIER)S0014-4827(20)30236-6 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.44 bkl Diehl, Markus I. verfasserin aut Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. Immune cell interactions Elsevier Macro Elsevier Cell cluster analysis Elsevier Cancer Elsevier Wolf, Steven P. oth Bindokas, Vytas P. oth Schreiber, Hans oth Enthalten in Academic Press 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS 2012 ECR Orlando, Fla (DE-627)ELV011050691 volume:393 year:2020 number:2 day:15 month:08 pages:0 https://doi.org/10.1016/j.yexcr.2020.112014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.44 Parasitologie Medizin VZ AR 393 2020 2 15 0815 0 |
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Enthalten in 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS Orlando, Fla volume:393 year:2020 number:2 day:15 month:08 pages:0 |
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Enthalten in 72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS Orlando, Fla volume:393 year:2020 number:2 day:15 month:08 pages:0 |
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72 OUTCOMES OF COMBINATION OF HEPATITIS B IMMUNOGLOBULIN AND HEPATITIS B VACCINATION IN HIGH-RISK NEWBORNS BORN TO HBEAG-POSITIVE MOTHERS |
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Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets |
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Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. |
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Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. |
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Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations. |
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